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Datasets:
TabBioMed
/
blood-transfusion-service

Tasks:
Tabular Classification
Modalities:
Tabular
Formats:
parquet
Size:
1K - 10K
Tags:
tabular
biomedical
benchmark
TabBioMed
clinical-&-healthcare
binary
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Polars
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row_idx
int64
f_0
float64
f_1
float64
f_2
float64
target
int64
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1
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blood-transfusion-service

Part of TabBioMed (TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning), a benchmark of 94 curated biomedical tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.

OpenML dataset (ID 1464), relation: blood-transfusion-service Monetary was removed because it equals 250 times Frequency. Exact duplicate rows were collapsed, and feature patterns with conflicting labels were dropped.

Domain Task Samples Features Classes
Clinical & Healthcare binary 471 3 2

Splits. 5 stored outer cross-validation folds (data/fold_<i>/), each with train/val/test files (fold 0: 329/47/95 rows). Every row appears in exactly one outer test fold. HPO in the paper uses inner folds drawn from each outer non-test pool.

Files

  • data/**.parquet: one row per sample. row_idx is the row index in the original TabBioMed array; f_<i>[_<name>] are the features (in order); target (or target_<j> for multi-target tasks) is the label. Categorical feature indices (0-based, among the features): []. Missing values are stored as nulls.
  • metadata.json: TabBioMed task metadata (task type, categorical indices, source, description).
from datasets import load_dataset
ds = load_dataset("TabBioMed/blood-transfusion-service")

A Croissant (1.1) description with Responsible-AI fields is distributed with the TabBioMed release.

Source and license

Source: OpenML_Dataset_ID_1464

This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also cite and respect the terms of the original source.

Citation 

@inproceedings{tabbiomed2026,
  title     = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
  author    = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}
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Number of rows:
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Total file size:
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