RESEARCH CENTRE

Inria Paris Centre

IN PARTNERSHIP WITH:

INSERM, Université Paris Cité

2023
ACTIVITY REPORT

Project-Team

HEKA

Health data- and model- driven
approaches for Knowledge Acquisition

IN COLLABORATION WITH: CENTRE DE RECHERCHE DES CORDELIERS

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Neuroscience and
Medicine

Contents

Project-Team HEKA

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 General aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Axis 1 - Knowledge extraction from clinical data . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Axis 2 - Stochastic and data-driven predictive modelling of health trajectories . . . . . . . .
3.2.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Axis 3: Data-driven and designs for next generation clinical trials . . . . . . . . . . . . . . . .
3.3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Multimodal approaches generalizable for several diseases . . . . . . . . . . . . . . . . . . . .
4.1.1 PEPR Digital Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.2 SurvivalGPU – “Using Graphics Processing Units (GPUs) to scale up survival analysis
to nation-wide cohorts" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.3 Messidore-Inserm BEEP - “Bayesian methods for Early Enriched Platform trials" . .
4.1.4 ANR AT2TA - “Analogies: from Theory to Tools and Applications"
. . . . . . . . . . .
4.1.5 HDH BOAS ADHERENCE - “Phenotyping algorithms for the assessment of thera-

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peutic adherence and its clinical consequences in the management of chronic diseases" 8

4.2 Cancer

ing in 2024)

4.1.6 iDEMO Meditwin - Dassault Systems - "Virtual twin for personalised medicine "(stat-
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 EraPerMed PeCAN (Personalized medicine in CANcer) (EU funding 2020-2023) . . .
4.2.2 SIRIC InsiTu - “Insights into cancer: from inflammation to Tumor" . . . . . . . . . .
4.2.3 Combo - Sanofi - "Evaluating drug combinations in oncology with Real-World Data

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and state-of-the-art knowledge" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.2.4 RHU OPERANDI - “Optimisation and imProved Efficacy of targeted RAdioNuclide

therapy in Digestive cancers by Imagomics" . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3 Rare diseases and pediatrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3.1 EU INVENTS Horizon project - “Innovative designs, extrapolation, simulation meth-
ods and evidence-tools for rare diseases addressing regulatory needs" (starting in
2024)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3.2 EU MSCA Doctoral Networks Orgestra project (starting 2024) . . . . . . . . . . . . . . 11
4.3.3 CIL LICO- Ciliopathies: group of disorders associated with genetic mutation leading
to rare and severe genetic diseases - Projet RHU - Recherche Hospitalo-Universitaire-
20118-22 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.3.4 BNDMR- Banque Nationale des Maladies Rares . . . . . . . . . . . . . . . . . . . . . . 11
. . . . . . . . . . . . . . . . . . 12
4.3.5 Genomic variability transversal program (2018-2023)
4.4 Other diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.4.1 Antibiotic resistance – FAIR project EU Horizon (on going)
. . . . . . . . . . . . . . . 12
4.4.2 Virtual reality (Ongoing) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5 Social and environmental responsibility

12
5.1 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

6 Highlights of the year

12
6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

7 New software, platforms, open data

13
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.1 medkit
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.2 Pythae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.3 Pyraug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

8 New results

14
8.1 Axis 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.2 Axis 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.3 Axis 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

9 Bilateral contracts and grants with industry

16
9.1 Bilateral Grants with Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

10 Partnerships and cooperations

10.1 European initiatives

17
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.1 Horizon Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.2 H2020 projects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.3 Other european programs/initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

11 Dissemination

17
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
11.1.1 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
11.1.2 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 18
11.1.3 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.2.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.2.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

12 Scientific production

12.1 Publications of the year

19
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

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Project-Team HEKA

Creation of the Project-Team: 2021 November 01

Keywords

Computer sciences and digital sciences

A3.3. – Data and knowledge analysis

A3.4. – Machine learning and statistics

A6.1. – Methods in mathematical modeling

A6.2. – Scientific computing, Numerical Analysis & Optimization

A9.1. – Knowledge

A9.2. – Machine learning

A9.4. – Natural language processing

A9.6. – Decision support

Other research topics and application domains

B2.2. – Physiology and diseases

B2.3. – Epidemiology

B2.6. – Biological and medical imaging

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Sarah Zohar [Team leader, INSERM, Senior Researcher, HDR]

• Adrien Coulet [INRIA, Associate Professor Detachement, HDR]

• Jean Feydy [INRIA, Researcher]

• Nicolas Garcelon [Fondation Imagine]

• Agathe Guilloux [INRIA, Professor Detachement, HDR]

• Claire Leconte Rives-Lange [INRIA, Advanced Research Position, from Oct 2023]

• Moreno Ursino [INSERM, Researcher]

Faculty Members

• Stephanie Allassonnière [UNIV PARIS CITE, Professor, HDR]

• François Angoulvant [Détachement Univ de Lausanne, Professor, HDR]

• Anita Burgun [UNIV PARIS CITE - APHP, Professor, HDR]

• David Drummond [UNIV PARIS CITE - APHP, Associate Professor]

• Anne-Sophie Jannot [UNIV PARIS CITE - APHP, HDR]

• Sandrine Katsahian [UNIV PARIS CITE - APHP, Professor, HDR]

• Antoine Neuraz [UNIV PARIS CITE - APHP until 31/12/2023]

• Bastien Rance [UNIV PARIS CITE - APHP, Associate Professor]

• Brigitte Sabatier [APHP, Professor, HDR]

Post-Doctoral Fellows

• Nadim Ballout [INRIA, Post-Doctoral Fellow]

• Sarah Berdot [APHP, Post-Doctoral Fellow]

• Sandrine Boulet [INSERM, Post-Doctoral Fellow]

• Xiaoyi Chen [Institut Imagine, until Aug 2023]

• Jong Ho Jhee [INRIA, Post-Doctoral Fellow, from Aug 2023]

• Germain Perrin [APHP, Post-Doctoral Fellow]

• Pierre Sabatier [APHP, until Aug 2023]

• Rosy Tsopra [UNIV PARIS CITE - APHP]

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PhD Students

• Safa Alsaidi [INRIA]

• Jean-Baptiste Baitairian [SANOFI, CIFRE, from Apr 2023]

• Nesrine Bannour [Univ Saclay]

• Linus Bleistein [ENS PARIS]

• Tom Boeken [UNIV PARIS CITE - APHP]

• Clément Chadebec [UNIV PARIS CITE, until Sep 2023]

• Pierre Clavier [LIX]

• Lea Comin [CEA, from Oct 2023]

• Aziliz Cottin [DASSAULT SYSTEMES, CIFRE]

• Charles De Ponthaud [UNIV PARIS - CITE, from Nov 2023]

• Benjamin Duputel [EXYSTAT, CIFRE, until Mar 2023]

• Thibaut Fabacher [UNIV STRASBOURG]

• Fabrice Gambaraza [APHP]

• Romain Jaquet [GHNE]

• Emilien Jemelen [Epiconcept, CIFRE, from Nov 2023]

• Enora Laas-Faron [INSTITUT CURIE]

• Judith Lambert [INSERM]

• Ivan Lerner [INSERM]

• Fabien Maury [INSERM, from Oct 2023]

• Juliette Murris [PIERRE FABRE, CIFRE]

• Lillian Muyama [INRIA]

• Sophie Quennelle [UNIV PARIS, until Nov 2023]

• Alice Rogier [INSERM, until 30/11/2023, then INRIA from 01/12/2023, until Nov 2023]

• Agathe Senellart [UNIV PARIS - CITE]

• Guillaume Serieys [UNIV PARIS - CITE]

• Stylianos Tzedakis [UNIV PARIS - CITE]

• Alexis Van Straaten [UNIV PARIS - CITE]

• Louis Vincent [IMPLICITY, CIFRE]

• Axel Vuorinen [INSERM, from Dec 2023]

Inria Annual Report 2023

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Technical Staff

• Deycy Camila Arias Villamil [INRIA, until 31/12/2023, Engineer]

• Armelle Arnoux [AP/HP]

• Olivier Birot [INRIA, until 31/12/2023, Engineer]

• Kim Tam Huynh [Inria, until 31/08/2023, Engineer]

• Louis Pujol [UNIV PARIS - CITE, Engineer]

Interns and Apprentices

• Killian Bakong Epoune [INSERM, Intern, until Jul 2023]

• Lea Comin [AMU, Intern, until Jul 2023]

• Naomi Daval-Pommier [UNIV PARIS - CITE, Intern, from Feb 2023 until Aug 2023]

• Maïsen Hassani [INRIA, Intern, from Apr 2023 until Sep 2023]

• Pierre Olivo-Marin [INSERM, Intern, from Feb 2023 until Aug 2023]

• Mohamed Raissi [INRIA, Intern, from Jun 2023 until Jul 2023]

• Axel Vuorinen [ENSAI, Intern, from Apr 2023 until Sep 2023]

Administrative Assistant

• Meriem Guemair [INRIA]

2 Overall objectives

2.1 Context

Clinicians routinely have to take decisions upon the diagnosis and treatment of complex patients for
which clinical guidelines do not provide clear recommendations or do not exist. This is particularly the
case for very heterogeneous diseases (e.g., rare diseases or cancer in which clinical manifestations or
response to treatment differ frequently from one individual to another) or when patients are seen for
a new emerging disease for which no recommendation has been established yet, as it was the case in
March, 2020 for the management of COVID-19 infections. Similar situations, leading to delicate decisions,
happened in the past but, unfortunately, this experience is hardly taken into account or rationalized
for clinicians. Indeed, data related to these past experiences are captured, but until now, these data
are not accessible to clinicians and not transformed into high level evidences. This level of evidence is
currently only reached by highly controlled analyses (such as controlled clinical trials), for which patients
might differ strongly from those treated in routine care, or might have never been seen before in the
case of new conditions. Besides, hospital information systems are used at every step of patient care,
collecting continuously longitudinal data, both unstructured and structured, including clinical reports,
drug prescriptions, physiology, laboratory results, imaging and omics data. These data may even be
enriched with medical wearable devices or large-scale claims data such as those of the French Health Data
Hub that informs on the global clinical course of patients. Likewise, progresses on artificial intelligence
approaches, such as supervised machine learning, have found applications in healthcare enabling for
instance clinical decision support systems, patient prioritization, drug repurposing or monitoring drug
safety. However, many particularities of health data (e.g., their sensitivity, noisiness, incompleteness,
heterogeneity and small volume when one very specific feature or several time points are required), and
particularities of health data analysis (e.g., the risk of confoundedness, the need for explanations and
fairness) make it challenging to develop tools that are both reliable and usable within hospital work-
and patient- flows. For instance, precision medicine requires stratifying smaller and smaller groups

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of patients, which may seem contradictory in regards with the general strategy of deep learning that
requires large amounts of data to be efficient. Another challenging task is the development of tools
that enable gaining knowledge from data agilely, i.e., to update knowledge gain continuously (without
compromising on reliability). In summary, methodological developments are required at each step of the
health data chain, including: (1) data access, (2) data transformation e.g., via representation learning, (3)
data analysis, predictive modelling and knowledge discovery with data- and model- driven approaches,
and (4) agile, fast and reliable access to data, implementation of these approaches through applications
such as decision support systems, medical devices, next generation clinical trials for the assessment of
medical knowledge.

2.2 General aim

The main objective of HeKA is to develop methodologies, tools and their applications in clinics towards a
learning health system, i.e., a health system that leverages clinical data collected to extract agilely and
reliably novel medical knowledge that, in turn, continuously improves healthcare. Indeed, the availability
of EHRs (Electronic Health Records), cohorts and other linked data such as the national Health Data Hub,
offers the opportunity to develop models for stratification and prediction with the potential of improving
the precision and the personalization of treatments, and thus the quality of healthcare.

3 Research program

The HeKA project-team is following 3 interdependent axes, that are (1) knowledge extraction from clinical
data, (2) stochastic and data-driven predictive modelling of health data, and (3) data-driven and designs
for next generation clinical trials. Theses axes can be view and interpreted form a patient care point of
view as (1) from patient data to patient representations, (2) from patient data to prediction and decision,
and (3) from models to improve patient-related knowledge, respectively. All these axes participate in the
development of a learning health system. Axes 1 and 2 can be related to observational studies (either
retrospective or prospective) and Axis 3 is related to interventional studies. As a remind, in observational
studies the investigator is not acting upon study participants, but only observing relationships between
factors and outcomes, while in interventional studies (i.e., clinical trials) the investigator intercedes as
part of the study design.

3.1 Axis 1 - Knowledge extraction from clinical data

The development of clinical decision support and statistical predictive models has been historically
made by manually selecting and tuning sets of predictive variables. This is a task-dependent and time-
consuming operation that neglects most of the available data. Real-word data, such as EHRs or cohorts,
offer an access to many variables, even those not initially thought of as predictive. For instance, EHRs
consist of structured data, such as demographics, diagnosis, procedures, biological laboratory results, and
medication exposures, which can be associated with unstructured data, such as clinical notes, discharge
summaries, pathology and imaging reports. In addition, this core EHR data may be complemented
with others, including images, omics data, patient-reported outcomes, or conversation transcriptions.
However, the use of EHR data for any precision medicine application represents an initial and significant
information extraction challenge because of their heterogeneity, incompleteness, and dynamic nature.
The aim of this axis is to develop methods and tools for leveraging patients’ data in their wide variety and
complexity. This encompasses the extraction and transformation of raw data into engineered, featured
and learned representations of good quality that will enable or facilitate the development of further
clinical decision support and knowledge discovery approaches, as those presented in Axes 2 and 3.

3.1.1 Methods

Methods developed in Axis 1 can be associated with three type of tasks: (i) deep phenotyping, (ii) patient
representation and (iii) reasoning with clinical knowledge. (i) Deep phenotyping consists in defining
algorithms that enable to identify patients with a particular and potentially complex profile within large
healthcare databases. It encompasses the development of natural language processing tools capable of

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Inria Annual Report 2023

extracting complex features and their context out of clinical texts; it also includes the ability to consider
simultaneously structured and unstructured data of these databases to identify relevant patients. To this
aim our methods rely on expert rules, distant-supervision and deep learning language representations.
(ii) Regarding patient representation we focus on two distinct kind of representations. The first one is an
explicit representation of patients in the form of knowledge graphs, using Semantic Web standards and
tools. The second one is a representation of patients within a latent space, using representation learning
methods largely inspired from results obtained by deep learning models to learn language representations.
(iii) Tasks concerned with reasoning on clinical knowledge are mutliple. It encompasses methods to
measure patients’ similarity between elaborated patients representations (either in the form of knowledge
graph or embeddings), hybrid approaches for analogical reasoning and logical and statistical inference.

3.2 Axis 2 - Stochastic and data-driven predictive modelling of health trajectories

The recent availability of high dimensional health data enables the emergence of data-driven models
for description, analyses with further possibility to guide clinical decisions. In this high dimensional
setting, machine learning-based prediction tasks have been demonstrated to be efficient, although they
may not be the best option in every setting. We are interested in these borders between settings, where
deep learning approaches fail, but alternatives succeed and reciprocally. Particularly, we are considering
borders found in temporal modelling and small-sample settings. Health data provided by EHRs have
several specificities among which: (i) patient care trajectories are high dimensional, and (ii) are censored,
i.e., data are observed until a certain timepoint. Current models do not succeed in simultaneously
tackling these two previous concerns. For example, patient trajectories include comorbidities pathways
where one disease may impact the others. Embedding patient trajectories in prognostic models remains
a challenge, especially, when low-sample-size high-dimension setting. Machine learning in such setting
should be adapted and compared with efficient statistical learning models.

3.2.1 Methods

During this year, we focused on adapting machine learning based methods on the following challenges:
signal detection using spatio-temporal data to be able to build health geographical vigilance system,
medical image classification in low sample setting, pharmacovigilance signal detection using large
database. We proposed a method to model and monitor population distributions over space and time, in
order to build an alert system for spatio-temporal data changes based on a new version of the Expectation-
Maximization (EM) algorithm to better estimate the number of clusters and their parameters at the
same time. We validate this approach on a real data set of positive diagnosed patients to coronavirus
disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic
changes. We proposed a new method to perform data augmentation in a reliable way in the High
Dimensional Low Sample Size setting using a geometry-based variational autoencoder. We validated this
approach on a medical imaging classification task where a small number of 3D brain magnetic resonance
images are considered and augmented using the proposed framework. We adapted a very classical
pharmacoepidemiological method to high dimensional setting, the Weigthed Cumulative Exposure
statistical model, which makes it possible to model the temporal relationship between the prescription of
a drug and a side effect, by implementing it using Graphics Processing Unit (GPU) programming. We
analysed several real life datasets using such implementation and show that it was possible using this
adaptation to apply this method to the National Health Insurance Database (SNDS).

3.3 Axis 3: Data-driven and designs for next generation clinical trials

New model-based fundamental researches could help in screening patients and predicting response prior
to clinical trials, thanks to biomarkers or EHRs based predictive modelling of patients’ response.Digital
Medical Devices, are health technologies falling into the definition of medical devices intending to
“Prevention, diagnosis, monitoring, treatment or alleviation of disease” (Regulation EU 2017/745) and
are mostly based on machine learning algorithms. Among them, learning software as medical devices
technologies (SaMD) now have the potential to be translated to patients’ care. As such, they should be
evaluated in clinical trials. However, by contrast to drugs for which the chemical formula does not change

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over time, the performances of a predictive model are constantly enriched by new observational data.
The challenge we face now is to be able at any time to ensure the safety and efficacy of SaMD and other
advances, for patient care. The objective of this axis is twofold : how clinical trials can help machine
learning? And inversely, how can machine learning and other information sources help clinical trials?
Accordingly, the first objective is to propose clinical trial methods and designs adapted to continuous
learning and adapting tools (DMDs). The second objective, is to produce innovative clinical trial designs
acquiring all possible patient-related knowledge: disease and translational models, EHRs, clinical trial
data and synthetic patients.

3.3.1 Methods

This year we focused more on the second objective. We developed (i) dose-finding methods that include
pharmacokinetics and pharmacodynamics modelling to better choose the appropriate dose-regimen
in oncology ; (II) methods based on logistic regression and random effects meta-analysis to syntetise
information from multiple early phase trials with several doses ; (iii)methods which allow for analsing
disruped clinical trial by COVID pandemic ; (iv) deep learning models that allow to analyse multistate
EHR ...

4 Application domains

4.1 Multimodal approaches generalizable for several diseases

4.1.1 PEPR Digital Health

The PEPR ("Programmes et équipements prioritaires de recherche") Digital Health aims at gathering
national multidisciplinary community active in digital health for the development and exploitation of the
concept of digital twin in health (started in September 2023).

HeKA’s involvement in this PEPR are the following; (i) within project ShareFAIR, to learn protocols
from clinical data collected along healthcare activity in Electronic Health Records (EHRs) to explicitize
the medical decision processes, (steps to reach a particular diagnosis or therapeutic choice) and the
management of particular conditions (steps in the management of a particular condition). Protocols
extracted from EHRs provide a view on the real-word clinical practice and may then be compared together
or with CPG (clinical practice guidelines) which can be seen as more theoretical protocols in that they
provide recommendations, or clinical pathways (CP) to standardize clinical practice. It will be applied
within NEUROVASC in the impact reduction of intracranial aneurysm and stroke, in which we will extract
and the proposed clinical pathways, (ii) within REWIND, to develop of new mathematical and statistical
approaches for the analysis of multimodal multiscale longitudinal data to predict patient’s response.
These models will be designed, implemented as prototypes and then transferred to an easy-used-well-
documented platform where people from diverse communities, in particular physicians, will be able to
use them on their own data set, (iii) within DIGPHAT, to develop Bayesian modelling of meta-models
pathways for the development of digital pharmacological twin; it consists in the analysis of data from
omics experiments and selection of relevant covariables and to combine meta-models in pathways to
select the most reliable twin model, (iv) within project M4DI, to develop a generic method for identifying
subgroups of patients with the same phenotype from health databases, using jointly variable correlations
and expert data, and to implement it within a computer package, (v) all these previous projects will
purpose models or Clinical Decision Support Systems (CDSS) to be translated to clinical practice, however,
proof based on data only is not sufficient and it should be evaluated in real life through prospective and
interventional clinical trials or studies, this will be done within SMATCH. In this project we will propose
new methodological paradigms for the clinical evaluation of Digital Medical Devices (DMD) including
CDSS and AI based models and algorithms.

4.1.2 SurvivalGPU – “Using Graphics Processing Units (GPUs) to scale up survival analysis to nation-

wide cohorts"

The recent availability of health insurance databases such as the SNDS opens the door to the detection of
adverse drug reactions in the general population. The aim is to generalize the survival analyses usually

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Inria Annual Report 2023

carried out during clinical trials on cohorts of N=1k to 10k patients to the full French population. This line
of research is appealing but poses real methodological challenges. Notably, it requires the development
of statistical analysis models that meet the robustness and interpretability requirements of public health
physicians while taking full advantage of recent hardware accelerators to scale up to millions of patients
per cohort. In this context, our team has been working since 2022 on an efficient re-implementation, on
Graphics Processing Units (GPUs), of the standard software tool in the field: the R package "survival".
The new "survivalGPU" library (https://github.com/jeanfeydy/survivalGPU) leverages recent
software tools (PyTorch, PyG, KeOps, reticulate) to bridge the gap between high-performance computing
and traditional survival analysis. It now provides a complete re-implementation of the Cox proportional
hazards model that is around 100 times faster on GPU than the survival package on CPU. Going further, it
supports time-varying drug exposures via the Weighted Cumulative Exposures model and is accessible
via an R interface which is fully retro-compatible with that of the survival package. We now intend to
perform extensive validation and comparison with other models, prior to pharmaco-epidemiological
studies on the SNDS data via the Health Data Hub platform.

4.1.3 Messidore-Inserm BEEP - “Bayesian methods for Early Enriched Platform trials"

The recent pandemic has shown the need of speeding up the clinical trial development of novel or repur-
posed therapies. Indeed, following the usual drug development paradigm, where clinical trial phases are
performed sequentially and separately, the time required to the full process easily exceeds a decade. Our
objective is to propose innovative Bayesian enriched “platform” designs for early phase trials, which are
adapted to the clinical context and go towards precision medicine. Since we are focusing on early phases
of clinical trial, in this setting “platform” cannot be linked to classical RCT. Thus, we aim at defining how
“platform” trial should be translated into these early phases. As in the original definition, early platform
phases will allow for flexibility, such as adding new arm or stopping treatments for futility (and/or safety
in our case). The word enriched refers to the use of new information, or at least not usually used in such
early trials, such as positron emission tomography (PET) scan, pharmacokinetics/pharmacodynamics
(PK/PD) modelling, mathematical modelling of immune responses, and to the enrichment of the enrolled
patient based on their biomarkers. The project is built around workpackages (WPs). In WP1, we develop
platform trials in phase 0/I, based on PET-scan; microdosing on several (preclinical) animal species
and humans will be adaptively compared, added or deleted to better characterize the extrapolation to
human. In WP2, we develop phase I/II dose-finding trial using PK/PD or mechanistic PD models. In
WP3, enrichment designs for phase I/II, in survival settings, are proposed when selected biomarkers are
available, and the design will be extended in case of combination therapies.

4.1.4 ANR AT2TA - “Analogies: from Theory to Tools and Applications"

Analogical reasoning is a kind of reasoning that is based on finding a common relational system between
two situations, exemplars, or domains. In computer science, analogical reasoning can be supported by
two main axes of artificial intelligence: knowledge representation and reasoning, and machine learning.
The AT2TA projet particularly aims at studying the role that machine learning can play in analogical
reasoning; and the HeKA team is in charge of exploring the application of their interplay in the healthcare
domain. A PhD student, co-supervised with Inria Paris, IHU Imagine and Université de Lorraine, is
learning representations of patients, relying on clinical texts, and study how these representations can
first compose analogical propositions, and second serve as bricks to a machine learning architecture for
analogical reasoning.

4.1.5 HDH BOAS ADHERENCE - “Phenotyping algorithms for the assessment of therapeutic adher-

ence and its clinical consequences in the management of chronic diseases"

Therapeutic adherence is a complex and multifactorial phenomenon that qualifies the degree to which the
patient conforms to medical prescriptions. This phenomenon has both detrimental clinical consequences
for the patient (in terms of prognosis and quality of life), and negative economic consequences for the
society. One difficulty in the study of adherence is that it measurement is mainly indirect and tainted
with many bias. Another difficulty is that adherence is not an end, but should be considered through the
lens of patient outcomes. The ADHERENCE project, funded under the Health Data Hub BOAS program

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(Bibliothèque Ouverte d’Algorithmes en Santé) aims at proposing clearly documented measurements
for the level of patient adherence and of its potential clinical outcomes in the context of three chronic
diseases (cancer, hypertension and transplantation). One challenge here is to enable these measurements
in both EHRs from hopsitals and claims from the national insurance database (SNDS).

4.1.6 iDEMO Meditwin - Dassault Systems - "Virtual twin for personalised medicine "(stating in 2024)

Meditwin is a collaborative project funded by BPI ("Banque Publique d’Investissement") with Dassault
Systems (leader of the projects), Inria, IHU institutes across France and Medtech startups. The aim of the
project is to provide a digital platform relying on virtual twins of individuals who faithfully reproduce
their state of health and which make it possible to test different therapeutic options. It will promote
interdisciplinarity by facilitating interoperability of multimodal medical data. Our team will use AI
approaches to propose Clinical Decision Support Systems (CDSS) in cardiovascular diseases and cancer.
We will also develop the clinical trial methodology evaluation these CDSS. In particular, HeKA will develop
stratification and classification algorithms, synthetics patient’s generators, statistical and mathematical
models for multi-modal and multidimensional health data and clinical evaluation methods for the
resulted CDSS as Digital Medical Devices.

4.2 Cancer

4.2.1 EraPerMed PeCAN (Personalized medicine in CANcer) (EU funding 2020-2023)

Breast cancer is the most common cancer in women worldwide. The most aggressive type is triple negative
breast cancer (TNBC), characterized by a very poor prognosis. New targeted and personalized therapies
are urgently required. In this context, the European project PeCAN aims at developing and assessing new
"AI algorithms" able to predict treatment response and personalize therapy of TNBC patients. They will
propose new approaches for personalization of drug therapy based on a deep molecular characterization
of individual tumors and patients and the establishment and use of digital medicine approaches to model
effects and side effects of all therapy options. In these projects, our team is in charge of developing multi-
scale models that integrate more clinical data in the models (as biomarkers, patients’ characteristics,
chemo and targeted molecule treatments, biology, clinical outcomes, etc.). We are designing a framework
for assessing the clinical performance and safety of the AI algorithms itself, by using real-world data
collected in clinical data warehouses and biobanks. These projects are associated with Axes 1-3.

4.2.2 SIRIC InsiTu - “Insights into cancer: from inflammation to Tumor"

To turn scientific knowledge into sustainable healthcare, cancer research must identify who is at risk of
cancer, when and in whom a new cancer arises, and how best to treat it and gauge response. Aligned with
Europe’s Beating Cancer Plan, InsiTu takes on the three challenges of cancer prevention, interception,
and treatment in digestive, lung, skin cancer, and heme malignancies. Chronic inflammation is a key
cancer niche fostering tumor initiation. Leveraging a transformative Tissue-Hub interfacing diagnostics
and research, our program ‘From inflammation to clonal emergence and cancer’ will unite experts in
chronic diseases damage to monitor patients with chronic tissue inflammation and cancer predisposition,
mirrored by animal modelling, to understand the critical transition from chronic tissue damage to cancer
progression, opening opportunities for prevention and interception. Such longitudinal (and sometimes
invasive) interactions between patients and healthcare practitioners can be improved by empowering
patients, taking psychic, social and ethical dimensions in consideration. Our program ‘Imaging cancer
and its environment’ will take a different approach to this challenge. Through synergetic interactions with
mathematicians and physicists, it will provide novel frameworks for multiscale integration of molecular
alterations, cellular processes, and tissue complexity. This effort will result in image- based, non-invasive
‘virtual biopsies’ as proxies of key biological processes underlying tumor heterogeneity and drug resist-
ance. Along with novel biomarkers such as circulating extracellular vesicles, these virtual biopsies will
gauge responses to new therapeutic approaches developed in our third program ‘From new targets to new
trials’. There, experts in leukemias and skin cancers will use cutting-edge in vivo functional screens and
multi-omic interrogation of Tissue-Hub samples to identify new targetable vulnerabilities and develop

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next-generation cell-based immunotherapies. To fasten the transfer of these innovations into care, new
adaptive clinical trial designs will be engineered.

4.2.3 Combo - Sanofi - "Evaluating drug combinations in oncology with Real-World Data and state-

of-the-art knowledge"

Combo is a collaborative project with Industry, national health data platforms and cancer institute:
Sanofi Pharma, The Health Data Hub, Centre Léon Berard and Inria-Inserm-HeKA. The objective of
the project is to identify promising families of drug combinations in oncology using multisource and
multi-modal data modelling and prediction, including RWD (cancer patients’ care data from CLCC cancer
centre), genomic public databases, literature, clinical trials depository and expert’s opinion. Once these
combinations will be identified mechanistic models will be used to determine dose-regimen and build
dose-finding trial designs for the combinations to be evaluated through formal clinical trials. In this
project we lead the following WPs (1) AI based analysis of the multimodal RWD and subgroup discovery
for the identification of relevant combinations, (2) Bayesian multi-modal analysis accounting RWD
modelling as well as expert’s opinion and literature and public clinical platforms AI analysis from Sanofi,
and (3) proposing candidates and designs for future phase I designs associated with dose, regimen and
associated molecule for the combination on selected family of combinations using preclinical PK/PD
model.

4.2.4 RHU OPERANDI - “Optimisation and imProved Efficacy of targeted RAdioNuclide therapy in

Digestive cancers by Imagomics"

Advanced stage hepatocellular carcinoma (HCC) and gastroenteropancreatic neuroendocrine tumours
(GEP-NET) are currently treated with targeted radionuclide therapy (TRT), a highly advanced method that
consists of either intra- arterial injections of radioactive microspheres (transarterial radioembolisation
- TARE) or targeted peptides radioactively labelled and administered systemically (Peptide Receptor
Radionuclide Therapy - PRRT). While highly effective, patient stratification and early identification
of responders are currently managed insufficiently due to the lack of pertinent imaging biomarkers,
either non-invasive or invasive. Furthermore, therapy-induced DNA damage leads to tumour resistance,
reducing TRT efficacy. We aim to overcome those current limits through the OPERANDI project via
innovative approaches in engineering, novel imaging biomarkers, and new concepts for DNA repair
mechanisms, combined with a fundamental understanding of causal links. Our ambitions go beyond
the current state-of-art, embracing even new combinations of drugs and α-emitters to enhance dose
localization and efficiency. Methodology will try to understand fundamentally whether current patient
management using CT/PET/MRI allows to predict response and survival using cutting edge imaging-
based artificial intelligence (AI) approaches in combination with data augmentation techniques to reach
statistical significance.

4.3 Rare diseases and pediatrics

4.3.1 EU INVENTS Horizon project - “Innovative designs, extrapolation, simulation methods and

evidence-tools for rare diseases addressing regulatory needs" (starting in 2024)

The evaluation of new medicines for rare diseases (RD) including rare paediatric RDs is challenging for
several reasons, among which are the small patient sample sizes, heterogeneity of patients and diseases
and heterogeneity in disease knowledge. Due to these difficulties, access to effective treatments and the
number of treatment options are often limited in RDs. INVENTS aims to provide clinical trial trialists,
researchers and regulators with a global framework encompassing methods, workflows and evidence
assessment tools to be implemented in orphan and paediatric drug development. Our ambition is to sig-
nificantly improve the evaluation of evidence and regulatory decision-making through the development
and validation of: refined longitudinal model-based diseases trajectories and treatment effect, improved
extrapolation models, in silico trials (e.g., virtual patient cohorts), optimised model-based clinical trial
designs and evidence synthesis methods. These will be evaluated through simulation studies and tested
on extensive data from a range of use cases provided by our industrial partners Roche and Novartis
and Real World data (RWD) from RD registry. The INVENTS framework will improve consistency and

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efficiency of the drug evaluation process for RD by augmenting clinical evidence without compromising
its scientific integrity and providing regulators assessment credibility criteria. At the end of this 5 years
project, the European industry will be able to exploit novel and improved clinical trial designs, in silico
trials and RWD analysis approaches supporting drug development in RD. The European Medicine Agency
and European national regulators (including Health Technology Assessment bodies) will be supplied
with a general framework allowing better informed decision making. Most importantly, RD patients will
benefit from an increased and faster access to efficacious and safe treatments.

4.3.2 EU MSCA Doctoral Networks Orgestra project (starting 2024)

Organoids experimental models are in vitro 3D cell cultures which can be generated from embryonic
stem cells, induced pluripotent stem cells or adult stem cells, and can replicate organs functionally and
structurally. Their physiological resemblance to target organs and ability to cryopreserve make organoids
a powerful tool for biomedical research and advancing understanding of the mechanisms underlying
certain disorders, including rare diseases. The ORGESTRA Joint Doctoral Network will propose innovative
organoid technologies for two genetic disorders, i.e., cystic fibrosis and cystinosis. In this project we
will supervise 2 PhDs project which will propose statistical development for; (1) linking in silico trials to
organoids data and innovative trial design. These designs will incorporate biomarkers-based findings, as
organoids, i.e., that reduce unnecessary exposure of patients (screening) or allow drugs to be screened
more effectively for non-effectiveness before embarking on human trials. This will be done via a joint
doctoral degree with University of Utrecht. (2) Estimand framework involving Bayesian principles on
organoids data for clinical trial outcomes and models. The estimands framework will be based on expert’s
elicitation to understand which questions are more relevant in term of clinical efficacy/toxicity, to select
the proper outcomes, to identify the possible intercurrent events and to provide a robust statistical model
whose parameters will be estimated under a Bayesian setting. This will be done via a joint doctoral degree
with Katholieke Universiteit Leuven.

4.3.3 CIL LICO- Ciliopathies: group of disorders associated with genetic mutation leading to rare
and severe genetic diseases - Projet RHU - Recherche Hospitalo-Universitaire- 20118-22

Ciliopathies are a large group of rare and severe genetic diseases caused by ciliary dysfunction associated
with clinical and genetic heterogeneity, as well as a lack of knowledge on patients’ natural history (i.e., the
evolution of the disease). The aim of this RHU-3 project, funded through the Investissements d’avenir
program, is to develop innovative, diagnostic, prognostic and tailored therapeutic approaches for patients
suffering from ciliopathies to prevent them from developing renal failure. Following this aim, we will
develop a mechanistic stratification of ciliopathies, in order to regroup suspected and already diagnosed
ciliopathies in a treatment-orientated classification. One goal is to engineer a ready-to-use bio-kit for
assessing both diagnosis and prognosis of developing renal alteration, in ciliopathy patients. Finally,
personalized therapeutic approaches will be proposed for patients using predictive approaches. We will
use methods developed in Axis 1 as well as methods proposed in Axis 2 to model patient trajectory. This
project is in collaboration with Imagine Institute, AP-HP, the Medetia company and Ecole Polytechnique.

4.3.4 BNDMR- Banque Nationale des Maladies Rares

The French National Registry for Rare Diseases (BNDMR) is a national tool for epidemiology and public
health purposes in the field of rare diseases. In line with the objectives defined by the 2nd and 3rd French
National Plan for Rare Diseases, the BNDMR team develops a secure national information system which
gathers anonymized clinical data of patients affected by rare diseases in its BNDMR data warehouse.
As medical head of the BNDMR, AS Jannot has several research projects strongly connected with HeKA
team including CDE.AI and Dromos project. CDE.ai aims to create a set of natural language processing
algorithms that will allow the semi-automatic completion of the rare disease minimal data set that is
currently completed manually for all patients followed up in the rare disease expert centres. In this
project, we will use methods developped in Axe 1 (collaboration with N Garcelon). The DROMOS project
is a project that uses the National Data Bank for Rare Diseases by linking it to health insurance data.
This matching will allow the description of the care of rare disease patients at the national level for

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Inria Annual Report 2023

rare diseases, including the characteristic care of the most frequent rare diseases. We will use methods
developped in the from of Axis 2 to model these longitudinal data.

4.3.5 Genomic variability transversal program (2018-2023)

The objective of the INSERM Genomic Variability cross-cutting programme is to understand the role
played by genes and their variants on the development of pathologies. This programme, which is based
on the longitudinal follow-up of cohorts of individuals and their phenotyping, aims to promote the
development of new methods for the analysis of longitudinal data. In this program, we develop in the
frame of axis 2 longitudinal approaches to define patients trajectories from truncated data such as those
in France National Health Database (SNDS).

4.4 Other diseases

4.4.1 Antibiotic resistance – FAIR project EU Horizon (on going)

The aim of the FAIR project is to evaluate Flagelin aerosol therapy for stimulation of immunity as an
alternative treatment against pneumonia with multidrug resistant bacteria. In this project, we are de-
veloping a full model using pharmacometrics expertise as well as statistical designs for extrapolation
purpose and the design of dose-finding study in healthy volunteers. As written above, in this project, S
Zoharvco-lead along with C Kloft (Freie Universitaet Berlin) the WP entitled “Development of a transla-
tional modelling and simulation platform for flagellin PK/PD”. The aim of the WP is to propose an optimal
design for the first-in-man clinical trials, maximizing knowledge gained from in vitro experimentation,
expert knowledge and pre-clinical experiments along the way. By incorporating mechanistic approaches
earlier in the development process along with a continuous learning modelling under Bayesian inference,
we hope to increase the probability of success of the translation process to the clinical setting and thus,
optimizing the statistical design and sample size. This project is in relation with axis 3.

4.4.2 Virtual reality (Ongoing)

Several projects led at the HEGP are currently ongoing to evaluate the analgesia provided by the use of
Virtual Reality in different care settings (extracorporeal lithotripsy, after colorectal cancer surgery, and
fiberoptic bronchoscopy in critical care). In these projects, not so close but still related to axis-2, we will
provide methodological approach and use statistical methods to conclude on the clinical questions, by
working closely with all Coordinating Investigators (Prof. D Clausse, G Manceau and A Rastello)

5 Social and environmental responsibility

5.1 Impact of research results

Our methods and designs are applied in collaboration with medical research team members at HEGP
and Necker hospitals (among others). During the last three years, and in consequence of the COVID-19
sanitary pandemic, we developed and deployed tools for knowledge extraction from clinical text to the
central clinical data warehouse of the AP-HP (Entrepôt de Données de Santé, AP-HP) . Through others,
these tools enable the recognition of named entities, their context (e.g., negation, personal or family
history, hypothesis). They have been widely reused for the development of clinical studies, which led
to 13 international publication of clinical epidemiology related to COVID-19. It also serve as a basis for
our implication in the 4CE international consortium (https://www.covidclinical.net/), which led to 15
publications. These tools have been refactored and their use is facilitate within the medkit library.

6 Highlights of the year

• David Drummond- nomination as MCU-PH in Sept 2023.

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• Stéphanie Allassonnière et Anne-Sophie Jannot - nomination at "Mission intergouvernemental sur

les données de santé"

• New fundings: PEPR Digital Health (2 projects as PI and 3 as WP leaders), EU Horizon cluster
Health as PI, EU MSCA as WP leader, idemo MEDITWIN (please see section Application domains
for details)

6.1 Awards

Jean Feydy: "prix science ouverte du logiciel libre de recherche 2023" du ministère de l’Enseignement
supérieur et de la Recherche, dans la catégorie "espoir - catégorie documentation"

7 New software, platforms, open data

7.1 New software

7.1.1 medkit

Name: a toolkit for a learning health system

Keywords: Learning health system, Biomedical data, Decision support, Python, Information extraction,

Natural language processing, Audio signal processing, Machine learning

Functional Description: This library aims at (1) facilitating the manipulation of healthcare data of
various modalities (e.g., structured, text, audio data) for the extraction of relevant features and (2)
developing supervized models from these various modalities for decision support in healthcare.

Release Contributions: This release 0.3.1 fixes a pip install issue found for release 0.3.0.

URL: https://github.com/TeamHeka/medkit

Contact: Adrien Coulet

Participants: Deycy Camila Arias Villamil, Olivier Birot, Kim-Tam Huynh, Antoine Neuraz, Ivan Lerner,

Bastien Rance, Adrien Coulet

7.1.2 Pythae

Keywords: Generative Models, Benchmarking, Reproducibility

Functional Description: This library implements some of the most common (Variational) Autoencoder
models under a unified implementation. In particular, it provides the possibility to perform
benchmark experiments and comparisons by training the models with the same autoencoding
neural network architecture. The feature make your own autoencoder allows you to train any of
these models with your own data and own Encoder and Decoder neural networks. It integrates
experiment monitoring tools such wandb, mlflow or comet-ml and allows model sharing and
loading from the HuggingFace Hub in a few lines of code.

URL: https://github.com/clementchadebec/benchmark_VAE

Contact: Clement Chadebec

7.1.3 Pyraug

Keywords: Generative Models, Data augmentation

Functional Description: This library provides a way to perform Data Augmentation using Variational
Autoencoders in a reliable way even in challenging contexts such as high dimensional and low
sample size data.

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Inria Annual Report 2023

URL: https://github.com/clementchadebec/pyraug

Contact: Clement Chadebec

8 New results

The team have generated many results in the last year, here are five illustrations for each axis.

8.1 Axis 1

Neuraz, A., Lerner, I., Birot, O., Arias, C., Han, L., Bonzel, C.L., Cai, T., Huynh K.T., Coulet, A. (2023).
TAXN: Translate Align Extract Normalize, a multilingual extraction tool for clinical texts. Studies in
health technology and informatics, To appear.

Several studies have shown that about 80% of the medical information in an electronic health record
is only available through unstructured data. Resources such as medical terminologies in languages
other than English are limited and restrain the NLP tools. We propose here to leverage English based
resources in other languages using a combination of translation, word alignment, entity extraction
and term normalization (TAXN). We implement this extraction pipeline in an opensource library called
“medkit”. We demonstrate the interest of this approach through a specific use-case: enriching a phenotypic
dictionary for post-acute sequelae in COVID-19 (PASC). TAXN proved to be efficient to propose new
synonyms of UMLS terms using a corpus of 70 articles in French with 356 terms enriched with at least
one validated new synonym. This study was based on freely available deeplearning models.

Participants: Neuraz A ., Lerner I ., Birot O ., Arias C ., Huynh KT ., Coulet A ..

8.2 Axis 2

We adapted and developed innovative methods based both on machine and deep learning for longitudinal
health data in high dimensional setting in different contexts including cluster detection using geographic
data, image classification, cut-point detection of prognostic factor, drug side effect detection.

Digital Respiratory Healthcare. Edited by Hilary Pinnock, Vitalii Poberezhets and David Drum-
mond. Book | Published in 2023 DOI: 10.1183/2312508X.erm10223 ISBN (electronic): 978-1-84984-
173-3

Respiratory care is undergoing a period of major change as it cautiously begins to embrace digital
transformation. Catalysed by the need for remote consultation in the pandemic, time-honoured ap-
proaches to delivering care are now being challenged by technology-based initiatives. This Monograph
deftly guides the reader through the potential benefits and pitfalls of such change, breaking the discussion
down into three areas: technological opportunities and regulatory challenges ; social benefits, challenges
and implications; exemplars of digital healthcare. Each chapter reviews contemporary literature and con-
siders not ‘if’ but ‘how’ a digital respiratory future can provide optimal care. The result is an authoritative,
balanced guide to developing digital respiratory health.

Participants: Drummond D ..

Lambert J, Leutenegger AL, Jannot AS, Baudot A. Tracking clusters of patients over time enables ex-
tracting information from medico-administrative databases. J Biomed Inform. 2023 Mar;139:104309.
doi: 10.1016/j.jbi.2023.104309. Identifying clusters (i.e., subgroups) of patients from the analysis of
medico-administrative databases is particularly important to better understand disease heterogeneity.
However, these databases contain different types of longitudinal variables which are measured over
different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering
approaches that can handle this type of data. We propose here cluster-tracking approaches to identify

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clusters of patients from truncated longitudinal data contained in medico-administrative databases.We
first cluster patients at each age. We then track the identified clusters over ages to construct cluster-
trajectories. We compared our novel approaches with three classical longitudinal clustering approaches
by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to
2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort. Our
cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance
without any imputation of data. The comparison of the silhouette scores obtained with the different
approaches highlights the better performances of the cluster-tracking approaches. The cluster-tracking
approaches are a novel and efficient alternative to identify patient clusters from medico-administrative
databases by taking into account their specificities.

Participants:

Lambert J ., Jannot AS ..

Korb-Savoldelli V, Tran Y, Perrin G, Touchard J, Pastre J, Borowik A, Schwartz C, Chastel A, Thervet E,
Azizi M, Amar L, Kably B, Arnoux A, Sabatier B. Psychometric Properties of a Machine Learning-Based
Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Obser-
vational Study. J Med Internet Res. 2023 This study aimed to create and validate a PROM on medication
adherence interpreted using an ML approach. This cross-sectional, single-center, observational study
was carried out a French teaching hospital between 2021 and 2022. Eligible patients must have had at
least 1 long-term treatment, medication adherence evaluation other than a questionnaire, the ability to
read or understand French, an age older than 18 years, and provided their nonopposition. Included adults
responded to an initial version of the PROM composed of 11 items, each item being presented using a
4-point Likert scale. The initial set of items was obtained using a Delphi consensus process. Patients
were classified as poorly, moderately, or highly adherent based on the results of a medication adherence
assessment standard used in the daily practice of each outpatient unit. An ML-derived decision tree was
built by combining the medication adherence status and PROM responses. Sensitivity, specificity, positive
and negative predictive values (NPVs), and global accuracy of the final 5-item PROM were evaluated. We
created an initial 11-item PROM with a 4-point Likert scale using the Delphi process. After item reduction,
a decision tree derived from 218 patients including data obtained from the final 5-item PROM allowed
patient classification into poorly, moderately, or highly adherent based on item responses. We developed
a medication adherence tool based on ML with an excellent NPV. This could allow prioritization pro-
cesses to avoid referring highly adherent patients to time- and resource-consuming interventions. The
decision tree can be easily implemented in computerized prescriber order-entry systems and digital
tools in smartphones. External validation of this tool in a study including a larger number of patients
with diseases associated with low medication adherence is required to confirm its use in analyzing and
assessing the complexity of medication adherence.

Participants:

Sabatier, P ., Perrin G ., JArnoux A ..

8.3 Axis 3

Duputel B, Stallard N, Montestruc F, Zohar S, Ursino M. Using dichotomized survival data to construct
a prior distribution for a Bayesian seamless Phase II/III clinical trial. Stat Methods Med Res. 2023
May;32(5):963-977. doi: 10.1177/09622802231160554. Master protocol designs allow for simultaneous
comparison of multiple treatments or disease subgroups. Master protocols can also be designed as
seamless studies, in which two or more clinical phases are considered within the same trial. They can
be divided into two categories: operationally seamless, in which the two phases are separated into
two independent studies, and inferentially seamless, in which the interim analysis is considered an
adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare
Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-
to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility)

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Inria Annual Report 2023

and predictive (selection) probabilities. The results of the first phase are then incorporated into prior
distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless
designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power
with respect to the operationally frequentist design.

Participants: Duputel B ., Zohar S ., Ursino M ..

Yap C, Rekowski J, Ursino M, Solovyeva O, Patel D, Dimairo M, Weir CJ, Chan AW, Jaki T, Mander
A, Evans TRJ, Peck R, Hayward KS, Calvert M, Rantell KR, Lee S, Kightley A, Hopewell S, Ashby D,
Garrett-Mayer E, Isaacs J, Golub R, Kholmanskikh O, Richards DP, Boix O, Matcham J, Seymour L, Ivy
SP, Marshall LV, Hommais A, Liu R, Tanaka Y, Berlin J, Espinasse A, de Bono J. Enhancing quality and
impact of early phase dose-finding clinical trial protocols: SPIRIT Dose-finding Extension (SPIRIT-
DEFINE) guidance. BMJ. 2023 Oct 20;383:e076386. doi: 10.1136/bmj-2023-076386. PMID: 37863491.
SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 provides guidance for
clinical trial protocol writing. However, neither the original guidance nor its extensions adequately cover
the features of early phase dose-finding trials. The SPIRIT Dose-finding Extension (DEFINE) statement
is a new guideline that provides recommendations for essential items that should be provided in the
protocols of these trials. It details extensions to the SPIRIT 2013 guidance, incorporating 17 new items
and modifying 15 existing items. The purpose of this guideline is to promote transparency, completeness,
reproducibility of methods, and interpretation of early phase dose-finding trial protocols. It is envisioned
that the resulting improvements in the design and conduct of early phase clinical trials will ultimately
reduce research inefficiencies and inconsistencies, driving transformational advances in clinical care.

Participants: Ursino M ..

Hua, W., Mei, H., Zohar, S., Giral, M., and Xu, Y. (2022). Personalized dynamic treatment regimes
in continuous time: a Bayesian approach for optimizing clinical decisions with timing. Bayesian
Analysis, 17(3), 849-878. We developed a two-step Bayesian approach to optimize clinical decisions,
estimating personalized optimal dynamic treatment regimes (DTRs), taking into account intervention
timing. In the first step, we build a generative model for a sequence of medical interventions with a
marked temporal point process (MTPP) where the mark is the assigned treatment or dosage. Then this
clinical action model is embedded into a Bayesian joint framework where the other components model
clinical observations including longitudinal medical measurements and time-to-event data conditional
on treatment histories. In the second step, we proposed a policy gradient method to learn the personalized
optimal clinical decision that maximizes the patient survival by interacting the MTPP with the model
on clinical observations while accounting for uncertainties in clinical observations learned from the
posterior inference of the Bayesian joint model in the first step.

9 Bilateral contracts and grants with industry

9.1 Bilateral Grants with Industry

• Dassault Systèmes - Cifre: S. Katsahian and A. Guilloux are supervising Aziliz Cottin on the project:

Survival dynamic prediction for personalization of cancer patients’ follow-up,

• Exystat - Cifre: S. Zohar and M. Ursino are co-supervising Benjamin Duputel on the project:

Platform Phase II/III seamless clinical trials Bayesian designs),

• Pierre Fabre - Cifre: S. Katsahian is supervising Juliette Murris on the project: prediction of large-

scale recurrent events in digestive cancers,

Project HEKA

17

• Implicity - Cifre: S. Allassonnière is supervising Louis Vincent on latent modelling of cardiac
time series including external information. A special focus is on missing data and heterogeneous
populations.

• Sanofi - Cifre S. Katsahian and A. Guilloux are co-supervising Jean-Baptiste Baitairian on the

project: Quantitative Bias Assessment for causal inference.

• Epiconcept - Cifre Sandrine Katsahian and Agathe Guilloux are co-supervising Emilien Jemelen on
the project: Evaluation of the contribution of the use of artificial intelligence in the French breast
cancer screening program.

• Combo - Sanofi - "Evaluating drug combinations in oncology with Real-World Data and state-of-
the-art knowledge". Adrien coulet, Moreno Ursino and Sarah Zohar are part of this project. Please
see section 4 for further details.

10 Partnerships and cooperations

10.1 European initiatives

10.1.1 Horizon Europe

INVENTS - Sarah Zohar is the PI of this EU Horizon project. Please see section 4 for further details.

10.1.2 H2020 projects

FAIR - Sarah Zohar is a WP leader in this H2020 project. Please see section 4 for further details.

10.1.3 Other european programs/initiatives

The European taskforce lead by EIT Health and French Ministry of Solidarity and Health, for the “har-
monization of clinical studies criteria and methodologies in Europe for the evaluation of digital medical
devices”. In this taskforce, Sarah Zohar co-lead the WP2 on “Evidence in clinical evaluation” with Corinne
Collignon (Head of the Digital Mission at HAS, France) and Barbara Höfgen (Head of the Unit DiGA-Fast-
Track at Bfarm, Germany)

EU MSCA Doctoral Networks -Project ORGESTRA "Organoid technologies for disease modeling, drug
discovery and development for rare diseases". Sarah Zohar is the WP leader of methodological design for
using organoids outcomes and transfer it to clinic.

10.2 National initiatives

ReCAP - Moreno Ursino is the referent of the « Early trials » group.The national network RECaP of Research
in Clinical Epidemiology and in Public Health has for purpose to mutualize original research projects and
to produce innovations in clinical epidemiology and public health.

11 Dissemination

11.1 Promoting scientific activities

11.1.1 Journal

Member of the editorial boards Sarah Zohar is Associated Editor of two scientific journals; "Biometrics"
and "Statistics in Biopharmaceutical Research"

Bastien Rance is member of the editorial board of the "International Journal ofMedical Informatics".

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Inria Annual Report 2023

Reviewer - reviewing activities Members ofHeKA are regular reviewers for the following journals: Scien-
tificData, Scientific Reports, SemanticWeb Journal, Journal of the SemanticWeb, Artificial Intelligence
inMedicine, statistics in medicine, biometrics, statistics in medical research, etc.

11.1.2 Leadership within the scientific community

Antia Burgun serves as the Representative of the French medical informatics community at the IMIA
(InternationalMedical Informatics Association). She also serves as a member of the Executive Board of
the Imagine Institute.

11.1.3 Scientific expertise

Sarah Zohar is a voting member at the Cnedimts (“Commission nationale d’évaluation des dispositifs
médicaux et des technologies de santé”) at HAS (“Haute Autorité de Santé”).

Sarah Zohar was in the review committee of BpiFrance grant evaluation "évaluation DM à base d’IA

et de numérique".

Members of the HeKA team are involved in several ethic committees, such as the Comité d’Ethique
de la Recherche APHP.Centre, the EDS AP-HP Comité Scientifique et Ethique. Members of HeKA the
scientific board of the ANR generic call Axe H.14 : Interfaces : mathématiques, sciences du numérique –
biologie, santé and of the FC3R (French Center for the 3R).

Stéphanie Allassonière and Anita Burgun hold the Helath Chair at PRAIRIE institute.
Stéphanie Allassonière is the vice-president for valorization of Université Paris Cité

11.2 Teaching - Supervision - Juries

Anne Sophie Jannot and Sandrine Katsahian lead the speciality “Big Data in Health” in theMaster of
Science of Public Health at the Paris Cité University.

Anne Sophie Jannot co-leads the quantitative biomedicine course, which is part from themedical

degree course.

Anne Sophie Jannot leads a professional degree of “health data reuse” at the Paris Cité University in

collaboration withMarseille and Bordeaux Universities.

Sandrine Katsahian is responsible for the PRIME department dedicated to the Research, Innovation,

DigitalMedicine located in the HEGP in APHP.Centre.

Moreno URSINO co-leads the course “Science des données” in the L2SIAS at the Paris Cité University.
Stéphanie Allassonniere within the MVA (Mathematics, Vision, Learning) M2 run a cours focusing on

the analysis of real-life health data.

Stéphanie Allassonniere coordinates the SMPS Bioentrepreneur (Université Paris Cité)
Jean Feydy is involved in the digital health track of the EPITA school of software enginee
Members of the HeKA team are in charge of two majors of the Master in Public Health of the Université

Paris Cité: one on Biomedical Informatics (IBM) and one on Data Science in Healthcare (DMS).

11.2.1 Internal or external Inria responsibilities

Stéphanie Allassonière was the chair of the CRCN-ISFP INRIA Lyon committee.

11.2.2 Interventions

HeKA memebers are regularly invited to present their research to the general public.

Project HEKA

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12 Scientific production

12.1 Publications of the year

International journals

[1] C. Bodemer, L. Soussand, A. Sandrin, A. Khatim, A. Sauvestre, N. Elarouci and A.-s. Jannot. ‘French
data on the epidemiology and expert healthcare network for epidermolysis bullosa’. In: Journal of
the European Academy of Dermatology and Venereology 37.5 (17th Jan. 2023). DOI: 10.1111/jdv.1
8826. URL: https://inria.hal.science/hal-04362512.

[2] T. Boeken, J. Feydy, A. Lecler, P. Soyer, A. Feydy, M. Barat and L. Duron. ‘Artificial intelligence in
diagnostic and interventional radiology: Where are we now?’ In: Diagnostic and Interventional
Imaging 104.1 (Jan. 2023), pp. 1–5. DOI: 10.1016/j.diii.2022.11.004. URL: https://hal.sci
ence/hal-03949288.

[3]

J. F. van Boven, D. Drummond, A. H. Chan, M. Hew, C. Y. Hui, I. Adejumo, I. Cano, K. Hansen,
V. Poberezhets, R. W. Costello and H. Pinnock. ‘ERS “CONNECT” Clinical Research Collaboration
– moving multiple digital innovations towards connected respiratory care: addressing the over-
arching challenges of whole systems implementation’. In: European Respiratory Journal 62.5 (Nov.
2023), p. 2301680. DOI: 10.1183/13993003.01680-2023. URL: https://hal.science/hal-04
339221.

[4] S. Calderazzo, S. Tarima, C. Reid, N. Flournoy, T. Friede, N. Geller, J. Rosenberger, N. Stallard,
M. Ursino, M. Vandemeulebroecke, K. van Lancker and S. Zohar. ‘Coping with Information Loss
and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on
Unplanned Clinical Trial Disruptions’. In: Statistics in Biopharmaceutical Research (26th June 2023),
pp. 1–17. DOI: 10.1080/19466315.2023.2211023. URL: https://hal.science/hal-0433680
4.

[5] G. Cheminet, A. Brunetti, D. Khimoud, B. Ranque, A. Michon, E. Flamarion, J. Pouchot, A.-s. Jannot
and J.-B. Arlet. ‘Acute chest syndrome in adult patients with sickle cell disease: The relationship with
the time to onset after hospital admission’. In: British Journal of Haematology 201.6 (25th Mar. 2023),
pp. 1229–1238. DOI: 10.1111/bjh.18777. URL: https://inria.hal.science/hal-04362502.

[6] M. Cosenza-Contreras, A. Schäfer, J. Sing, L. Cook, M. Stillger, C.-Y. Chen, J. Villacorta Hidalgo,
N. Pinter, L. Meyer, T. Werner, D. Bug, Z. Haberl, O. Kübeck, K. Zhao, S. Stei, A. V. Gafencu, R.
Ionita, F. Brehar, J. Ferrer-Lozano, G. Ribas, L. Cerdá-Alberich, L. Martí-Bonmatí, C. Nimsky, A. van
Straaten, M. Biniossek, M. Föll, N. Cabezas-Wallscheid, J. Büscher, H. Röst, A. Arnoux, J. Bartsch
and O. Schilling. ‘Proteometabolomics of initial and recurrent glioblastoma highlights an increased
immune cell signature with altered lipid metabolism’. In: Neuro-Oncology (26th Oct. 2023). DOI:
10.1093/neuonc/noad208. URL: https://hal.science/hal-04343649.

[7] A. Decoux, L. Duron, P. Habert, V. Roblot, E. Arsovic, G. Chassagnon, A. Arnoux and L. Fournier.
‘Comparative performances of machine learning algorithms in radiomics and impacting factors’.
In: Scientific Reports 13.1 (28th Aug. 2023), p. 14069. DOI: 10.1038/s41598-023-39738-7. URL:
https://hal.science/hal-04343652.

[8] R. Degrave, J. Murris, A. Charles-Nelson, O. Hermine, R. Porcher, P. Ravaud, X. Mariette, P.-L.
Tharaux, M. Resche-Rigon, O. Sanchez, S. Katsahian and J.-B. Arlet. ‘Risk factors for thromboem-
bolic events in patients hospitalized for COVID-19 pneumonia in a general ward and requiring
treatment with oxygen’. In: Postgraduate Medical Journal (17th Nov. 2023). DOI: 10.1093/postmj
/qgad104. URL: https://hal.science/hal-04348667.

[9] M.-A. Delbarre, F. Girardon, L. Roquette, P. Blanc-Durand, M.-A. Hubaut, É. Hachulla, F. Semah,
D. Huglo, N. Garcelon, E. Marchal, I. El Esper, C. Tribouilloy, N. Lamblin, P. Duhaut, J. Schmidt,
E. Itti and T. Damy. ‘Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake
at Risk of Cardiac Amyloidosis’. In: JACC: Cardiovascular Imaging (Apr. 2023). DOI: 10.1016/j.jc
mg.2023.01.014. URL: https://u-picardie.hal.science/hal-04082960.

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Inria Annual Report 2023

[10] A. Dhote, S. Tzedakis, C. Hobeika, U. Marchese, R. Coriat, A. Nassar, A. Beaufrère, T. Codjia, C.
Goumard, O. Scatton, A. Dohan, M. Ronot, B. Terris, M. Lesurtel, S. Dokmak, F. Cauchy and D. Fuks.
‘Impact of induction therapy in locally advanced intrahepatic cholangiocarcinoma’. In: EJSO -
European Journal of Surgical Oncology (May 2023). DOI: 10.1016/j.ejso.2023.05.014. URL:
https://hal.science/hal-04172878.

[11] D. Drummond and A. Gonsard. ‘Télésurveillance dans l’asthme pédiatrique : vers des systèmes
cyber-physiques’. In: Perfectionnement en Pédiatrie 6.4 (Dec. 2023), pp. 244–249. DOI: 10.1016/j
.perped.2023.10.009. URL: https://hal.science/hal-04339220.

[12] D. Drummond, J. Roukema and M. Pijnenburg. ‘Home monitoring in asthma: towards digital
twins’. In: Current Opinion in Pulmonary Medicine 29.4 (27th July 2023), pp. 270–276. DOI: 10.109
7/MCP.0000000000000963. URL: https://hal.science/hal-04339229.

[13] D. Drummond, P. Whelan and M. Sperrin. ‘Association between nebuliser therapies adherence and
visit-to-visit variability of FEV1 in patients with cystic fibrosis’. In: Journal of Cystic Fibrosis 22.4
(July 2023), pp. 702–705. DOI: 10.1016/j.jcf.2023.03.006. URL: https://hal.science/hal-
04339233.

[14] B. Duputel, N. Stallard, F. Montestruc, S. Zohar and M. Ursino. ‘Using dichotomized survival data
to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial’. In: Statistical
Methods in Medical Research 32.5 (May 2023), pp. 963–977. DOI: 10.1177/09622802231160554.
URL: https://hal.science/hal-04126394.

[15] L. Farah, J. Murris, I. Borget, A. Guilloux, N. Martelli and S. I. Katsahian. ‘Assessment of Performance,
Interpretability, and Explainability in Artificial Intelligence–Based Health Technologies: What
Healthcare Stakeholders Need to Know’. In: Mayo Clinic Proceedings: Digital Health 1.2 (June 2023),
pp. 120–138. DOI: 10.1016/j.mcpdig.2023.02.004. URL: https://hal.science/hal-04348
677.

[16] C. Faviez, M. Talmatkadi, P. Foulquié, A. Mebarki, S. Schück, A. Burgun and X. Chen. ‘Assessment
of the Early Detection of Anosmia and Ageusia Symptoms in COVID-19 on Twitter: Retrospective
Study’. In: JMIR Infodemiology 3 (2023), e41863. DOI: 10.2196/41863. URL: https://inria.hal
.science/hal-04227117.

[17] L. Gaucher, P. Sabatier, S. Katsahian and A.-S. Jannot. ‘Pharmacovigilance studies without a priori
hypothesis: systematic review highlights inappropriate multiple testing correction procedures’. In:
Journal of Clinical Epidemiology 162 (Oct. 2023), pp. 127–134. DOI: 10.1016/j.jclinepi.2023
.08.010. URL: https://hal.science/hal-04348670.

[18] A. Gonsard, R. Aboutaam, B. Prévost, C. Roy, A. Hadchouel, N. Nathan, J. Taytard, A. Pirojoc, C.
Delacourt, S. Wanin and D. Drummond. ‘Children’s views on artificial intelligence and digital
twins for the daily management of their asthma: a mixed-method study’. In: European Journal of
Pediatrics 182.2 (Feb. 2023), pp. 877–888. DOI: 10.1007/s00431-022-04754-8. URL: https://h
al.science/hal-03993835.

[19] E. Gras, A. Jean, V. Rocher, Y. Tran, S. Katsahian, D. Jouclas, C. Dano, J. Cedile, D. Manar, N. Kassis
Chikhani, J. Le Guen, C. Patas D’illiers and D. Lebeaux. ‘Incidence of and risk factors for local
complications of peripheral venous catheters in patients older than 70 years: Empirical research
quantitative’. In: Journal of Clinical Nursing 32.15-16 (Aug. 2023), pp. 5000–5009. DOI: 10.1111/jo
cn.16732. URL: https://hal.science/hal-04348675.

[20] P. Habert, A. Decoux, L. Chermati, L. Gibault, P. Thomas, A. Varoquaux, F. Le Pimpec-Barthes, A.
Arnoux, L. Juquel, K. Chaumoitre, S. Garcia, J.-Y. Gaubert, L. Duron and L. Fournier. ‘Best imaging
signs identified by radiomics could outperform the model: application to differentiating lung
carcinoid tumors from atypical hamartomas’. In: Insights into Imaging 14.1 (19th Sept. 2023),
p. 148. DOI: 10.1186/s13244-023-01484-9. URL: https://hal.science/hal-04343651.

[21] B. Hansel, P. Sabatier, D. Kadouch, T. Poghosyan, K. Arapis, C. Rives-Lange, S. Czernichow, A.-S.
Jannot and C. Carette. ‘Bariatric Surgery Should Be Proposed in Certain Septuagenarian Patients
with Obesity’. In: Obesity Surgery 33.8 (22nd June 2023), pp. 2608–2610. DOI: 10.1007/s11695-02
3-06655-7. URL: https://inria.hal.science/hal-04362497.

Project HEKA

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[22] E. Kempf, M. Vaterkowski, D. Leprovost, N. Griffon, D. Ouagne, S. Breant, P. Serre, A. Mouchet,
B. Rance, G. Chatellier, A. Bellamine, M. Frank, J. Guerin, X. Tannier, A. Livartowski, M. Hilka
and C. Daniel. ‘How to Improve Cancer Patients ENrollment in Clinical Trials From rEal-Life
Databases Using the Observational Medical Outcomes Partnership Oncology Extension: Results of
the PENELOPE Initiative in Urologic Cancers’. In: JCO Clinical Cancer Informatics 7 (May 2023).
DOI: 10.1200/CCI.22.00179. URL: https://hal.science/hal-04107871.

[23] V. Korb-Savoldelli, Y. Tran, G. Perrin, J. Touchard, J. Pastre, A. Borowik, C. Schwartz, A. Chastel,
E. Thervet, M. Azizi, L. Amar, B. Kably, A. Arnoux and B. Sabatier. ‘Psychometric Properties of a
Machine Learning–Based Patient-Reported Outcome Measure on Medication Adherence: Single-
Center, Cross-Sectional, Observational Study’. In: Journal of Medical Internet Research 25 (16th Oct.
2023), e42384. DOI: 10.2196/42384. URL: https://hal.science/hal-04343657.

[24]

J. Lambert, A.-L. Leutenegger, A.-S. Jannot and A. Baudot. ‘Tracking clusters of patients over time
enables extracting information from medico-administrative databases’. In: Journal of Biomedical
Informatics 139 (Mar. 2023), p. 104309. DOI: 10.1016/j.jbi.2023.104309. URL: https://hal
.science/hal-04027783.

[25] A. Lansier, C. Bourillon, C.-A. Cuénod, E. Ragot, A. Follin, S. Hamada, O. Clément, P. Soyer and
A.-S. Jannot. ‘CT-based diagnostic algorithm to identify bowel and/or mesenteric injury in patients
with blunt abdominal trauma’. In: European Radiology 33 (2023), pp. 1918–1927. DOI: 10.1007/s0
0330-022-09200-9. URL: https://hal.science/hal-03883935.

[26] V. Maisons, Y. Ramdani, A. Hankard, C. Messiaen, A.-S. Jannot, B. Sautenet, J.-M. Halimi, F. Maillot,
É. Pillebout and A. Audemard-Verger. ‘New insights into epidemiological data and impact of the
COVID-19 pandemic on IgA vasculitis in children and adults: a French nationwide cohort’. In:
Rheumatology International 43.10 (12th July 2023), pp. 1791–1798. DOI: 10.1007/s00296-023-05
387-2. URL: https://inria.hal.science/hal-04362465.

[27] R. Marcilly, W.-Y. Zheng, P. Quindroit, S. Pelayo, S. Berdot, B. Charpiat, J. Corny, S. Drouot, P. Frery,
G. Leguelinel-Blache, L. Mondet, A. Potier, L. Robert, L. Ferret and M. Baysari. ‘Comparison of the
validity, perceived usefulness, and usability of I-MeDeSA and TEMAS, two tools to evaluate alert
system usability’. In: International Journal of Medical Informatics 175 (July 2023), p. 105091. DOI:
10.1016/j.ijmedinf.2023.105091. URL: https://hal.science/hal-04097607.

[28] P. Martin, C. Alberti, A. Bourmaud, A. Burgun and E. de La Rochebrochard. ‘Communauté interact-
ive et participative en ligne, menée avec les pairs, comme outil de promotion de la santé sexuelle et
reproductive des adolescents et jeunes adultes: la recherche-action Sexpairs’. In: Epidemiology and
Public Health = Revue d’Epidémiologie et de Santé Publique 71.Supplement 2 (May 2023), pp. 6–7.
DOI: 10.1016/j.respe.2023.101609. URL: https://hal.science/hal-04208892.

[29] M. Mayenga, N. Falvo, I. Mahé, A.-S. Jannot, B. Gazeau, G. Meyer, N. Gendron, O. Sanchez, S.
Djennaoui and B. Planquette. ‘Cancer-Associated Thrombosis on Bevacizumab: Risk of Recurrence
and Bleeding When Bevacizumab Is Stopped or Continued’. In: Cancers 15.15 (31st July 2023),
p. 3893. DOI: 10.3390/cancers15153893. URL: https://inria.hal.science/hal-04362460.

[30] A. Mouazer, N. Boudegzdame, K. Sedki, R. Tsopra and J.-B. Lamy. ‘Model and Strategy for Predicting
and Discovering Drug-Drug Interactions’. In: Studies in Health Technology and Informatics. Studies
in Health Technology and Informatics (18th May 2023). DOI: 10.3233/SHTI230248. URL: https:
//hal.science/hal-04162795.

[31]

J. Murris, A. Charles-Nelson, A. Tadmouri Sellier, A. Lavenu and S. Katsahian. ‘Towards filling the
gaps around recurrent events in high dimensional framework: a systematic literature review and
application’. In: Biostatistics & Epidemiology 7.1 (22nd Nov. 2023). DOI: 10.1080/24709360.2023
.2283650. URL: https://hal.science/hal-04320229.

[32] T. Pichon, C. Messiaen, L. Soussand, C. Angin, A. Sandrin, N. Elarouci and A.-S. Jannot. ‘Overview of
patients’ cohorts in the French National rare disease registry’. In: Orphanet Journal of Rare Diseases
18.1 (3rd July 2023), p. 176. DOI: 10.1186/s13023-023-02725-2. URL: https://inria.hal.sc
ience/hal-04362496.

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Inria Annual Report 2023

S. Quennelle and D. Bonnet. ‘Pediatric heart failure with preserved ejection fraction, a review’. In:
Frontiers in Pediatrics 11 (2nd Aug. 2023). DOI: 10.3389/fped.2023.1137853. URL: https://ha
l.science/hal-04191461.

[34] C. Rives-Lange, S. Czernichow and A.-S. Jannot. ‘Is Maternal Metabolic Bariatric Surgery the Best
Solution to Tackle the Childhood Obesity Pandemic?—Reply’. In: JAMA Surgery 158.8 (1st Aug.
2023), p. 892. DOI: 10.1001/jamasurg.2023.0931. URL: https://inria.hal.science/hal-0
4362499.

[35] M. Sapoval, C. Querub, H. Pereira, O. Pellerin, T. Boeken, A. Di Gaeta, M. A. Ahmar, M.-M. Lefevre-
Colau, C. Nguyen, C. Daste, M. Lacroix, J.-D. Laredo, B. Sabatier, N. Martelli, G. Chatellier, C. Déan
and F. Rannou. ‘Genicular artery embolization for knee osteoarthritis: Results of the LipioJoint-1
trial’. In: Diagnostic and Interventional Imaging (Dec. 2023). DOI: 10.1016/j.diii.2023.12.003.
URL: https://hal.science/hal-04349672.

[36]

S. Schmartz, A. Welfringer-Morin, M. Le Bourgeois, C. Delacourt, L. Berteloot, S. H. Rabia, N. Bellon,
F. Moulin, M. Pouletty, N. Garcelon, C. Bodemer and D. Drummond. ‘Respiratory complications
in pediatric epidermal necrolysis: A retrospective study of 22 cases’. In: Journal of The American
Academy of Dermatology 89.4 (Oct. 2023), pp. 857–859. DOI: 10.1016/j.jaad.2023.05.092. URL:
https://hal.science/hal-04339228.

[37] F. Schortgen, C. Tabra Osorio, D. Carpentier, M. Henry, P. Beuret, G. Lacave, G. Simon, P.-Y. Blan-
chard, T. Gobe, A. Guillon, L. Bitker, G. Duhommet, J.-P. Quenot, M. Le Meur, S. Jochmans, F.
Dubouloz, N. Mainguy, J. Saletes, T. Creutin, P. Nicolas, J. Senay, A.-L. Berthelot, D. Rizk, D. Tran Van,
A. Riviere, S. B. Heili-Frades, J. Nunes, N. Robquin, S. Lhotellier, S. Ledochowski, A. Guénégou-
Arnoux and A. Constan. ‘Fluid Intake in Critically Ill Patients: The “Save Useless Fluids For Intensive
Resuscitation” Multicenter Prospective Cohort Study’. In: Critical Care Medicine (1st Nov. 2023).
DOI: 10.1097/CCM.0000000000006091. URL: https://hal.science/hal-04343611.

[38] D. Sciard, K. Xu, A. Soulier, G. Dufour, M. Ursino, C. Alberti and M. Beaussier. ‘Postoperative
analgesia after surgical repair of distal radius fracture: a randomized comparison between distal
peripheral nerve blockade and surgical site infiltration’. In: Minerva Anestesiologica 89.10 (Oct.
2023). DOI: 10.23736/s0375-9393.23.16956-2. URL: https://hal.science/hal-04336790.

[39] D. Smadja, A.-S. Jannot, A. Philippe, E. Lu, J. Rancic, O. Sanchez, R. Chocron, N. Gendron and J.-L.
Diehl. ‘Circulating Von Willebrand factor: a consistent biomarker predicting in-hospital mortality
across different waves of the COVID-19 pandemic’. In: Angiogenesis (9th Dec. 2023). DOI: 10.1007
/s10456-023-09901-9. URL: https://inria.hal.science/hal-04358149.

[40] O. Solovyeva, M. Dimairo, C. Weir, S. W. Hee, A. Espinasse, M. Ursino, D. Patel, A. Kightley, S. Hughes,
T. Jaki, A. Mander, T. Evans, S. Lee, S. Hopewell, K. R. Rantell, A.-W. Chan, A. Bedding, R. Stephens,
D. Richards, L. Roberts, J. Kirkpatrick, J. de Bono and C. Yap. ‘Development of consensus-driven
SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study’. In: BMC
Medicine 21.1 (5th July 2023), p. 246. DOI: 10.1186/s12916-023-02937-0. URL: https://hal.s
cience/hal-04336816.

[41]

S. Tan, G. Goudot, A. Arnoux, Y. Tran, H. Maissoro, G. Poenou, G. Detriche, L. Khider, N. Mohamedi,
T. Mirault, A. Galloula and E. Messas. ‘Occurrence of Major Local Lower Limb Events in Type
2 Diabetic Patients with Lower Extremity Arterial Disease: Impact of Metformin’. In: Annals of
Vascular Surgery 90 (Mar. 2023), pp. 153–161. DOI: 10.1016/j.avsg.2022.09.064. URL: https:
//hal.science/hal-04343656.

[42] R. Tsopra, N. Peiffer-Smadja, C. Charlier, F. Campeotto, C. Lemogne, P. Ruszniewski, B. Vivien and
A. Burgun. ‘Putting undergraduate medical students in AI-CDSS designers’ shoes: An innovative
teaching method to develop digital health critical thinking’. In: International Journal of Medical
Informatics 171 (Mar. 2023), 10 p. DOI: 10.1016/j.ijmedinf.2022.104980. URL: https://hal
.science/hal-04162799.

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[43] S. Tzedakis, O. Yzhaky Shapira, M. Schwarzinger, S. Katsahian, A. Lazzati, A. Dohan, R. Coriat,
P. Sogni, S. Pol, D. Fuks, V. Mallet, U. Marchese, S. Bouam and M. Alifano. ‘COVID-19 and excess
mortality of patients with liver cancer in France, January 2020–September 2022’. In: British Journal
of Surgery (10th Nov. 2023). DOI: 10.1093/bjs/znad359. URL: https://hal.science/hal-043
48669.

[44] A. Vanier, J. Fernandez, S. Kelley, L. Alter, P. Semenzato, C. Alberti, S. Chevret, D. Costagliola, M.
Cucherat, B. Falissard, F. Gueyffier, J. Lambert, E. Lengliné, C. Locher, F. Naudet, R. Porcher, R.
Thiébaut, M. Vray, S. Zohar, P. Cochat and D. Le Guludec. ‘Rapid access to innovative medicinal
products while ensuring relevant health technology assessment. Position of the French National
Authority for Health’. In: BMJ Evidence-Based Medicine (14th Feb. 2023), bmjebm-2022–112091. DOI:
10.1136/bmjebm-2022-112091. URL: https://univ-rennes.hal.science/hal-04056273.

[45] A. Welfringer-Morin, P. Bataille, D. Drummond, N. Bellon, S. Ingen-Housz-Oro, J. Bonigen, S.
Schmartz, L. Giraud-Kerleroux, F. Moulin, L. de Saint Blanquat, D. Bremond-Gignac, S. Hadj-Rabia
and C. Bodemer. ‘Comparison of idiopathic and drug-induced epidermal necrolysis in children’. In:
British Journal of Dermatology 189.5 (25th Nov. 2023), pp. 631–633. DOI: 10.1093/bjd/ljad226.
URL: https://hal.science/hal-04339226.

[46] C. Yap, J. Rekowski, M. Ursino, O. Solovyeva, D. Patel, M. Dimairo, C. Weir, A.-W. Chan, T. Jaki,
A. Mander, T. Evans, R. Peck, K. Hayward, M. Calvert, K. R. Rantell, S. Lee, A. Kightley, S. Hopewell,
D. Ashby, E. Garrett-Mayer, J. Isaacs, R. Golub, O. Kholmanskikh, D. Richards, O. Boix, J. Matcham,
L. Seymour, S. P. Ivy, L. Marshall, A. Hommais, R. Liu, Y. Tanaka, J. Berlin, A. Espinasse and J. de
Bono. ‘Enhancing quality and impact of early phase dose-finding clinical trial protocols: SPIRIT
Dose-finding Extension (SPIRIT-DEFINE) guidance’. In: BMJ - British Medical Journal (20th Oct.
2023), e076386. DOI: 10.1136/bmj-2023-076386. URL: https://hal.science/hal-04336820.

[47] C. Yap, O. Solovyeva, J. de Bono, J. Rekowski, D. Patel, T. Jaki, A. Mander, T. Evans, R. Peck, K.
Hayward, S. Hopewell, M. Ursino, K. R. Rantell, M. Calvert, S. Lee, A. Kightley, D. Ashby, A.-W. Chan,
E. Garrett-Mayer, J. Isaacs, R. Golub, O. Kholmanskikh, D. Richards, O. Boix, J. Matcham, L. Seymour,
S. P. Ivy, L. Marshall, A. Hommais, R. Liu, Y. Tanaka, J. Berlin, A. Espinasse, M. Dimairo and C. Weir.
‘Enhancing reporting quality and impact of early phase dose-finding clinical trials: CONSORT
Dose-finding Extension (CONSORT-DEFINE) guidance’. In: BMJ - British Medical Journal (20th Oct.
2023), e076387. DOI: 10.1136/bmj-2023-076387. URL: https://hal.science/hal-04336824.

[48] C. Zamboglou, J. Peeken, A. Janbain, S. Katsahian, I. Strouthos, K. Ferentinos, A. Farolfi, S. Koerber,
J. Debus, M. Vogel, S. Combs, A. Vrachimis, A. G. Morganti, S. Spohn, M. Shelan, D. Aebersold,
A.-L. Grosu, F. Ceci, C. Henkenberens, S. Kroeze, M. Guckenberger, S. Fanti, C. Belka, P. Bartenstein,
G. Hruby, S. Scharl, T. Wiegel, L. Emmett, A. Arnoux and N.-S. Schmidt-Hegemann. ‘Development
and Validation of a Multi-institutional Nomogram of Outcomes for PSMA-PET–Based Salvage
Radiotherapy for Recurrent Prostate Cancer’. In: JAMA Network Open 6.5 (23rd May 2023), e2314748.
DOI: 10.1001/jamanetworkopen.2023.14748. URL: https://hal.science/hal-04343654.

International peer-reviewed conferences

[49] L. Bleistein, A. Fermanian, A.-S. Jannot and A. Guilloux. ‘Learning the Dynamics of Sparsely Ob-
served Interacting Systems’. In: PMLR - Proceedings of Machine Learning Research. ICML 2023 -
40th International Conference on Machine Learning. Vol. 202. Honolulu, Hawaii, United States,
15th June 2023. URL: https://hal.science/hal-04336559.

[50] L. Caquelin, P. Badra, M. Ursino, B. Laviolle and C. Locher. ‘Meta-analysis of phase I dose-finding
studies: Application for protein kinase inhibitors developed in oncology’. In: EPICLIN 2023 17e
Conférence francophone d’Épidémiologie Clinique - 30e Journées des statisticiens des Centres de
Lutte contre le Cancer. Fundamental and clinical pharmacology. Villers-lès-Nancy, France: Wiley,
2023. DOI: 10.1016/j.respe.2023.101816. URL: https://hal.science/hal-04266628.

[51] T. Fabacher, E.-A. Sauleau, N. Leclerc, H. Bergier, J.-E. Gottenberg, A. Coulet and A. Névéol. ‘Evalu-
ating the Portability of Rheumatoid Arthritis Phenotyping Algorithms: case study on French EHRs’.
In: Stud Health Technol Inform. Medical Informatics Europe. 302. Gothenburg, Sweden, 18th May
2023, pp. 768–772. URL: https://hal.science/hal-04069779.

24

Inria Annual Report 2023

[52] A. Neuraz, I. Lerner, O. Birot, C. Arias, L. Han, C. L. Bonzel, T. Cai, K. T. Huynh and A. Coulet. ‘TAXN:
Translate Align Extract Normalize, a multilingual extraction tool for clinical texts’. In: MedInfo 2023
– the 19th world congress on Medical and Health Informatics. Syndney, Australia, 8th July 2023. URL:
https://inria.hal.science/hal-04069590.

[53] M. A. N. Pour, A. Algergawy, P. Buche, L. J. Castro, J. Chen, A. Coulet, J. Cufi, H. Dong, O. Fallatah,
D. Faria, I. Fundulaki, S. Hertling, Y. He, I. Horrocks, M. Huschka, L. Ibanescu, D. S. Jain, E. Jiménez-
Ruiz, N. Karam, P. Lambrix, H. Li, Y. Li, P. Monnin, E. Nasr, H. Paulheim, C. Pesquita, T. Saveta,
P. Shvaiko, G. Sousa, C. Trojahn, J. Vatašˇcinová, M. Wu, B. Yaman, O. Zamazal and L. Zhou. ‘Results
of the Ontology Alignment Evaluation Initiative 2023’. In: Proceedings of the 18th International
Workshop on Ontology Matching co-located with the 22nd International Semantic Web Conference
(ISWC 2023), Athens, Greece, November 7, 2023. 18th International Workshop on Ontology Matching
(OM 2023). Vol. 3591. CEUR workshop proceedings. Athens, Greece: CEUR-WS.org, 7th Nov. 2023,
pp. 97–139. URL: https://inria.hal.science/hal-04366893.

[54] K. Sedki, N. Boudegzdame, J. Lamy and R. Tsopra. ‘Learning Preferences in Lexicographic Choice
Logic’. In: Proceedings of the 15th International Conference on Agents and Artificial Intelligence
- (Volume 3). ICAART 2023 - 15th International Conference on Agents and Artificial Intelligence.
Lisbonne, Portugal: SCITEPRESS - Science and Technology Publications, 1st Jan. 2023, pp. 1012–
1019. DOI: 10.5220/0011891300003393. URL: https://hal.science/hal-04162803.

[55] F. Sverrisson, M. Akdel, D. Abramson, J. Feydy, A. Goncearenco, Y. Adeshina, D. Kovtun, C. Marquet,
X. Zhang, D. Baugher, Z. Carpenter, L. Naef, M. M. Bronstein and B. Correia. ‘DiffMaSIF: Surface-
based Protein-Protein Docking with Diffusion Models’. In: Machine Learning in Structural Biology
workshop at NeurIPS 2023. New Orleans, United States, 15th Dec. 2023. URL: https://hal.scien
ce/hal-04360638.

National peer-reviewed Conferences

[56] N. Bannour, X. Tannier, B. Rance and A. Névéol. ‘Positionnement temporel indépendant des
évènements : application à des textes cliniques en français’. In: Actes de CORIA-TALN 2023. Actes de
la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 2 : travaux
de recherche originaux – articles courts. 18e Conférence en Recherche d’Information et Applications
– 16e Rencontres Jeunes Chercheurs en RI – 30e Conférence sur le Traitement Automatique des
Langues Naturelles – 25e Rencontre des Étudiants Chercheurs en Informatique pour le Traitement
Automatique des Langues. Paris, France: ATALA, 2023, pp. 1–14. URL: https://hal.science/hal
-04130201.

Scientific books

[57] H. Pinnock, V. Poberezhets and D. Drummond. Digital Respiratory Healthcare. European Respirat-
ory Society, 1st Dec. 2023. DOI: 10.1183/2312508X.erm10223. URL: https://hal.science/ha
l-04339219.

Scientific book chapters

[58] S. Quennelle, M. Douillet, L. Friedlander, O. Boyer, A. Neuraz, A. Burgun and N. Garcelon. ‘The
Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection
During Clinical Trials’. In: Caring is Sharing – Exploiting the Value in Data for Health and Innovation.
Vol. 302. Studies in Health Technology and Informatics. 18th May 2023. DOI: 10.3233/SHTI230112.
URL: https://hal.science/hal-04191423.

Edition (books, proceedings, special issue of a journal)

[59] A. Coulet, C. Gérardin, A. Névéol and X. Tannier, eds. Proceedings of workshop on Patient Similarity,

SimPa 2023. 13th Mar. 2023. URL: https://inria.hal.science/hal-04080808.

Project HEKA

Reports & preprints

25

[60] C. Chadebec and S. Allassonnière. Variational Inference for Longitudinal Data Using Normalizing

Flows. 13th Sept. 2023. URL: https://hal.science/hal-04206103.

[61]

J. Lambert, A.-L. Leutenegger, A. Baudot and A.-S. Jannot. Improving patient clustering by incorpor-
ating structured label relationships in similarity measures. 10th June 2023. DOI: 10.1101/2023.06
.06.23291031. URL: https://hal.science/hal-04208019.

[62] A. Senellart, C. Chadebec and S. Allassonnière. MultiVae: A Python library for Multimodal Generative

Autoencoders. 14th Sept. 2023. URL: https://hal.science/hal-04207151.

[63] A. Senellart, C. Chadebec, S. Allassonnière and S. Allassonnière. Improving Multimodal Joint
Variational Autoencoders through Normalizing Flows and Correlation Analysis. 16th May 2023. URL:
https://hal.science/hal-04207134.
<scratchpad>
- Main topic: Research Centre HEKA (Health Data- and Model- driven approaches for Knowledge Acquisition)
- Key points:
  - HEKA project-team focuses on developing methodologies, tools, and applications in clinics towards a learning health system.
  - The team follows three interdependent axes: knowledge extraction from clinical data, stochastic and data-driven predictive modeling of health data, and data-driven and designs for next generation clinical trials.
  - HEKA aims to develop methods and tools for leveraging patients' data in their wide variety and complexity.
  - The team has involvement in various projects, including PEPR Digital Health, SurvivalGPU, Messidore-Inserm BEEP, and EU INVENTS Horizon project.
- Structure: The document is divided into sections, including Project-Team, Activity Report, Contents, Research Program, Application Domains, Social and Environmental Responsibility, Highlights of the Year, New Software, Platforms, Open Data, New Results, Bilateral Contracts and Grants with Industry, Partnerships and Cooperations, Dissemination, Scientific Production, and National Initiatives.
- Potential artifacts to ignore: The document contains web-related elements such as links, HTML tags, and other web-related elements that should be disregarded.
</scratchpad>

<final_summary>
The HEKA project-team is focused on developing methodologies, tools, and applications in clinics towards a learning health system. The team follows three interdependent axes: knowledge extraction from clinical data, stochastic and data-driven predictive modeling of health data, and data-driven and designs for next generation clinical trials. HEKA aims to leverage patients' data in their wide variety and complexity, and has involvement in various projects, including PEPR Digital Health, SurvivalGPU, Messidore-Inserm BEEP, and EU INVENTS Horizon project. The team's work has significant implications for the development of precision medicine and the improvement of healthcare outcomes. By developing methods and tools for knowledge acquisition, HEKA is contributing to the creation of a learning health system that can provide personalized and effective care to patients.
The HEKA project-team is focused on developing methodologies, tools, and applications in clinics towards a learning health system. The team follows three interdependent axes: knowledge extraction from clinical data, stochastic and data-driven predictive modeling of health data, and data-driven and designs for next generation clinical trials. HEKA aims to leverage patients' data in their wide variety and complexity, and has involvement in various projects, including PEPR Digital Health, SurvivalGPU, Messidore-Inserm BEEP, and EU INVENTS Horizon project. The team's work has significant implications for the development of precision medicine and the improvement of healthcare outcomes. By developing methods and tools for knowledge acquisition, HEKA is contributing to the creation of a learning health system that can provide personalized and effective care to patients.