| --- |
| 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} |
| } |
| ``` |
|
|
