Datasets:

tarekmasryo
/

blood-donation-registry-dataset

pretty_name: Blood Donation Registry — Synthetic Donors, Prevalence & Compatibility
license: cc-by-4.0
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - blood-donation
  - transfusion
  - healthcare-operations
  - decision-support
  - risk-scoring
  - calibration
  - thresholding
  - synthetic
  - tabular-data
annotations_creators:
  - no-annotation
source_datasets:
  - synthetic
configs:
  - config_name: ml_ready
    data_files:
      - split: train
        path: data/blood_donation_registry_ml_ready.csv
  - config_name: population_distribution
    data_files:
      - split: train
        path: data/blood_population_distribution.csv
  - config_name: compatibility_lookup
    data_files:
      - split: train
        path: data/blood_compatibility_lookup.csv
  - config_name: data_dictionary
    data_files:
      - split: train
        path: data/data_dictionary.csv

🩸 Blood Donation Registry — Synthetic Donors, Prevalence & Compatibility

Synthetic, decision-focused tables for blood donation operations: donor eligibility/deferrals, donation history, rare blood types, country-level prevalence, and RBC transfusion compatibility (ABO/Rh).

Synthetic data (safe for experimentation and teaching)
Not clinical/medical ground truth — do not use for real-world medical decisions.

📦 What’s inside

This repo provides four loadable dataset configs via datasets.load_dataset:

ml_ready (default / recommended) → donor-level ML-ready table
population_distribution → country-level blood type prevalence
compatibility_lookup → RBC transfusion compatibility matrix (8×8)
data_dictionary → column-level documentation (all files)

🚀 Quick start (Hugging Face)

from datasets import load_dataset

repo_id = "tarekmasryo/blood-donation-registry-dataset"

donors = load_dataset(repo_id, "ml_ready")["train"].to_pandas()
pop    = load_dataset(repo_id, "population_distribution")["train"].to_pandas()
compat = load_dataset(repo_id, "compatibility_lookup")["train"].to_pandas()

# Example join: add country population + Rh- rate to each donor
donors = donors.merge(
    pop[["country_code", "population_size", "rh_negative_rate"]],
    on="country_code",
    how="left",
)
print(donors.shape)
print(donors.head(3))

Tip: load_dataset(repo_id) will load the first config (here: ml_ready).

🗂️ Data files

1) `data/blood_donation_registry_ml_ready.csv` (30,000 rows × 27 columns)

Donor-level snapshot with a fixed reference date:

as_of_date = 2024-12-31

Key groups

Identity & geography: donor_id (unique), country_code, region
Profile: age, sex (M/F), bmi, smoker (0/1), chronic_condition_flag (0/1)
Eligibility & deferrals: eligibility_status, eligible_to_donate (0/1), deferral_reason
Donation behavior/history: donation_count_last_12m, lifetime_donation_count, first_donation_year, years_since_first_donation, last_donation_date, recency_days, is_regular_donor (0/1), donor_age_at_first_donation, preferred_site
Blood context: blood_type (8 types), is_rare_type (0/1), blood_type_country_prevalence
Engineered score (optional): donation_propensity_score

Outcome columns

donated_next_6m (0/1)
next_6m_donation_count (0–3)

2) `data/blood_population_distribution.csv` (39 rows × 12 columns)

Country-level population + blood type prevalence:

keys: country_code, region
population_size
proportions: p_o_pos, p_o_neg, p_a_pos, p_a_neg, p_b_pos, p_b_neg, p_ab_pos, p_ab_neg
rh_negative_rate

Integrity expectation: blood type proportions sum to 1.0 per country.

3) `data/blood_compatibility_lookup.csv` (64 rows × 4 columns)

RBC transfusion compatibility matrix:

donor_blood_type, recipient_blood_type
compatible_for_rbc_transfusion (0/1)
compatibility_level: ideal | acceptable | incompatible

4) `data/data_dictionary.csv`

Column-level documentation for all files:

file, column_name, type, description, allowed_values_or_range, missing_values

🔗 Relationships

blood_donation_registry_ml_ready.country_code ↔ blood_population_distribution.country_code
blood_donation_registry_ml_ready.blood_type_country_prevalence is derived from the matching country prevalence proportions.
blood_compatibility_lookup provides rule-based compatibility for donor/recipient blood type pairs.

🧪 Recommended tasks

1) Donation likelihood (binary classification)

Target: donated_next_6m
Suggested evaluation: ROC-AUC, PR-AUC, F1 + calibration (reliability curve / Brier score)

2) Donation frequency (count prediction)

Target: next_6m_donation_count
Suggested evaluation: MAE, RMSE (optional Poisson/ordinal baselines)

3) Decision policy under constraints

Turn probabilities into an outreach policy:

choose an operating threshold under capacity/budget
compare FP/FN tradeoffs
validate stability across segments (region, rare types, eligibility)

4) Rare-blood operations analytics

analyze is_rare_type by country prevalence
explore compatibility-aware matching using the lookup matrix

🧯 Modeling notes (avoid leakage / shortcuts)

donated_next_6m is derived from next_6m_donation_count → use one outcome as the target.
eligible_to_donate overlaps with eligibility_status → keep one for simpler baselines.
eligible_to_donate == 0 implies donated_next_6m == 0 → for behavior modeling, consider training on eligible_to_donate == 1.
donation_propensity_score is engineered; exclude it for “feature-only” benchmark baselines.

✅ Data quality expectations

donor_id is unique (no duplicate donors)
no duplicate rows
snapshot consistency (as_of_date fixed)
recency_days aligns with as_of_date - last_donation_date
country codes match the prevalence table keys
compatibility lookup covers all 8×8 donor/recipient pairs

🧬 Synthetic data generation (high-level)

Records are simulated to reflect realistic constraints and patterns:

eligibility rules and deferral reasons (age/BMI/health flags)
donation history distributions and recency behavior
country-level blood type prevalence used to derive per-donor prevalence context
ABO/Rh compatibility rules encoded in the lookup table

Synthetic distributions may not match any specific real-world population.

⚠️ Limitations

snapshot-style dataset (not a full longitudinal event log beyond history fields)
synthetic distributions may differ from real operational settings
engineered signals (e.g., donation_propensity_score) can act as shortcut features if used without care

🧾 Citation

Tarek Masryo. (2025). Blood Donation Registry — Synthetic Donors, Prevalence & Compatibility.

📜 License

CC BY 4.0 — attribution required.

👤 Author

Tarek Masryo