---
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.

```python
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

```bibtex
@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}
}
```