Datasets:

TabBioMed
/

VitalDB

license: cc-by-4.0
pretty_name: VitalDB
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

VitalDB

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.

VitalDB perioperative in-hospital mortality classification using 57 features available by the end of the operative/anesthesia case. ICU stay, hospital length of stay, admission/discharge timestamps and patient/case identifiers are excluded from X. Device/procedure indicators, grouped diagnoses and perioperative durations are retained; two invalid anesthesia durations are missing. ASA and laboratory missingness are preserved for training-fitted imputation. The stored patient-grouped, outcome-stratified fixed split is unchanged: 4,442/978/968 training/validation/test cases, containing 41/9/7 mortality-positive cases (57 cases from 48 patients). The small event counts limit validation and test precision.

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	6,388	57	2

Splits. Fixed train/val/test split (4442/978/968 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): [37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 7, 53, 6, 5]. 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.
sample_metadata.csv: per-row provenance (identifiers, source annotations); not features.

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

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

Source and license

Source: VitalDB

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}
}