tarekmasryo/blood-donation-registry-dataset

🩸 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.

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📦 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)

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🚀 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).

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🗂️ 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)

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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.

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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

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4) data/data_dictionary.csv

Column-level documentation for all files:

• file, column_name, type, description, allowed_values_or_range, missing_values

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🔗 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.

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🧪 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

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🧯 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.

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✅ 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

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🧬 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.

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⚠️ 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

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🧾 Citation

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

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📜 License

CC BY 4.0 — attribution required.

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👤 Author

Tarek Masryo