| --- |
| 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. |
| |
| ```python |
| 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 |
| |
| ```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} |
| } |
| ``` |
| |
