Datasets:

TabBioMed
/

IACCD

license: odc-by
pretty_name: IACCD
task_categories:
  - tabular-classification
tags:
  - tabular
  - biomedical
  - benchmark
  - TabBioMed
  - clinical-&-healthcare
  - binary
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: validation
        path: data/val.parquet
      - split: test
        path: data/test.parquet

IACCD

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.

Indwelling Arterial Catheter Clinical Dataset (IACCD) for 28-day mortality prediction. TabBioMed uses the separately released PhysioNet mimic2-iaccd dataset, available without credentialed access under the Open Data Commons Attribution License v1.0; it does not directly access the credentialed MIMIC-II database. The whole-stay blood-gas test count (abg_count) is excluded from predictors.

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	1,776	34	2

Splits. Fixed train/val/test split (1243/266/267 rows).

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): [0, 2, 9, 10, 11, 12, 13, 14, 15, 16, 17, 7, 8]. Missing values are stored as nulls.
metadata.json: TabBioMed task metadata (task type, categorical indices, source, description).
feature_names.json: original feature names, in column order.

from datasets import load_dataset
ds = load_dataset("TabBioMed/IACCD")

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

Source and license

Source: PhysioNet mimic2-iaccd v1.0

This derived dataset is distributed under ODC-By 1.0 (https://opendatacommons.org/licenses/by/1-0/). 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}
}