---
license: cc-by-4.0
pretty_name: MUSIC
task_categories:
- tabular-classification
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- clinical-&-healthcare
- classification
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: validation
    path: data/val.parquet
  - split: test
    path: data/test.parquet
---

# MUSIC

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.

MUSIC cause-of-death classification. Eleven patients lost to follow-up (exit code 1) and twenty heart-transplant patients (exit code 2) are excluded because no cause of death was observed. Class 0 is alive at the end of follow-up. Cause codes 1, 3, and 6/7 map to classes 1, 2, and 3.

| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Clinical & Healthcare | classification | 961 | 65 | 4 |

**Splits.** Fixed train/val/test split (672/144/145 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): `[1, 8, 9, 10, 11, 12, 40, 43, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 7, 13, 14, 15, 46, 47]`. 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/MUSIC")
```

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

## Source and license

Source: MUSIC

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}
}
```