| --- |
| 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 |
| license: cc-by-4.0 |
| --- |
| |
| # 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 |
|
|
| Dataset license: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). |
|
|
| - [VitalDB v1.0.0](https://physionet.org/content/vitaldb/1.0.0/). Hyung-Chul Lee and Chul-Woo Jung (2022), PhysioNet DOI 10.13026/czw8-9p62, publication DOI 10.1038/s41597-022-01411-5. Source license: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Retain attribution and the license link, identify modifications, and do not imply endorsement. Cite PhysioNet: Goldberger et al. (2000), DOI 10.1161/01.CIR.101.23.e215. |
|
|
| See `NOTICE.md` for attribution and modifications and `LICENSES/` for license texts. |
|
|
| ## 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} |
| } |
| ``` |
|
|
