Datasets:
File size: 2,726 Bytes
4c1c4a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ---
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}
}
```
|