TabBioMed camera-ready release

README.md

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+---
+license: cc-by-4.0
+pretty_name: audiology
+task_categories:
+- tabular-classification
+tags:
+- tabular
+- biomedical
+- benchmark
+- TabBioMed
+- clinical-&-healthcare
+- binary
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: fold_0_train
+    path: data/fold_0/train.parquet
+  - split: fold_0_validation
+    path: data/fold_0/val.parquet
+  - split: fold_0_test
+    path: data/fold_0/test.parquet
+  - split: fold_1_train
+    path: data/fold_1/train.parquet
+  - split: fold_1_validation
+    path: data/fold_1/val.parquet
+  - split: fold_1_test
+    path: data/fold_1/test.parquet
+  - split: fold_2_train
+    path: data/fold_2/train.parquet
+  - split: fold_2_validation
+    path: data/fold_2/val.parquet
+  - split: fold_2_test
+    path: data/fold_2/test.parquet
+  - split: fold_3_train
+    path: data/fold_3/train.parquet
+  - split: fold_3_validation
+    path: data/fold_3/val.parquet
+  - split: fold_3_test
+    path: data/fold_3/test.parquet
+  - split: fold_4_train
+    path: data/fold_4/train.parquet
+  - split: fold_4_validation
+    path: data/fold_4/val.parquet
+  - split: fold_4_test
+    path: data/fold_4/test.parquet
+---
+
+# audiology
+
+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.
+
+OpenML Audiology ID 999, version 2: binary classification of the original cochlear_age diagnosis (P, encoded 1) versus all other source diagnoses (N, encoded 0). The P/N mapping is verified against the row-aligned original multiclass release, OpenML ID 7. The negative class includes other hearing disorders and normal ears. Exact duplicate feature rows with agreeing labels are collapsed before splitting; missing categorical measurements are preserved. Evaluation uses five stratified outer folds and three-fold inner cross-validation for hyperparameter selection.
+
+| Domain | Task | Samples | Features | Classes |
+|---|---|---|---|---|
+| Clinical & Healthcare | binary | 199 | 69 | 2 |
+
+**Splits.** 5 stored outer cross-validation folds (`data/fold_<i>/`), each with train/val/test files (fold 0: 139/20/40 rows). Every row appears in exactly one outer test fold. HPO in the paper uses inner folds drawn from each outer non-test pool.
+
+## 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, 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]`. 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/audiology")
+```
+
+A Croissant (1.1) description with Responsible-AI fields is distributed with the
+TabBioMed release.
+
+## Source and license
+
+Source: OpenML_Dataset_ID_999
+
+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}
+}
+```

data/fold_0/test.parquet

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data/fold_0/val.parquet

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feature_names.json

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+[
+  "age_gt_60",
+  "air",
+  "airBoneGap",
+  "ar_c",
+  "ar_u",
+  "bone",
+  "boneAbnormal",
+  "bser",
+  "history_buzzing",
+  "history_dizziness",
+  "history_fluctuating",
+  "history_fullness",
+  "history_heredity",
+  "history_nausea",
+  "history_noise",
+  "history_recruitment",
+  "history_ringing",
+  "history_roaring",
+  "history_vomiting",
+  "late_wave_poor",
+  "m_at_2k",
+  "m_cond_lt_1k",
+  "m_gt_1k",
+  "m_m_gt_2k",
+  "m_m_sn",
+  "m_m_sn_gt_1k",
+  "m_m_sn_gt_2k",
+  "m_m_sn_gt_500",
+  "m_p_sn_gt_2k",
+  "m_s_gt_500",
+  "m_s_sn",
+  "m_s_sn_gt_1k",
+  "m_s_sn_gt_2k",
+  "m_s_sn_gt_3k",
+  "m_s_sn_gt_4k",
+  "m_sn_2_3k",
+  "m_sn_gt_1k",
+  "m_sn_gt_2k",
+  "m_sn_gt_3k",
+  "m_sn_gt_4k",
+  "m_sn_gt_500",
+  "m_sn_gt_6k",
+  "m_sn_lt_1k",
+  "m_sn_lt_2k",
+  "m_sn_lt_3k",
+  "middle_wave_poor",
+  "mod_gt_4k",
+  "mod_mixed",
+  "mod_s_mixed",
+  "mod_s_sn_gt_500",
+  "mod_sn",
+  "mod_sn_gt_1k",
+  "mod_sn_gt_2k",
+  "mod_sn_gt_3k",
+  "mod_sn_gt_4k",
+  "mod_sn_gt_500",
+  "notch_4k",
+  "notch_at_4k",
+  "o_ar_c",
+  "o_ar_u",
+  "s_sn_gt_1k",
+  "s_sn_gt_2k",
+  "s_sn_gt_4k",
+  "speech",
+  "static_normal",
+  "tymp",
+  "viith_nerve_signs",
+  "wave_V_delayed",
+  "waveform_ItoV_prolonged"
+]

metadata.json

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+{
+  "READY": true,
+  "name": "audiology",
+  "task": "binary",
+  "num_classes": 2,
+  "cat_idx": [
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+    68
+  ],
+  "num_samples": 199,
+  "num_features": 69,
+  "imbalance_ratio": 3.5227,
+  "description": "OpenML Audiology ID 999, version 2: binary classification of the original cochlear_age diagnosis (P, encoded 1) versus all other source diagnoses (N, encoded 0). The P/N mapping is verified against the row-aligned original multiclass release, OpenML ID 7. The negative class includes other hearing disorders and normal ears. Exact duplicate feature rows with agreeing labels are collapsed before splitting; missing categorical measurements are preserved. Evaluation uses five stratified outer folds and three-fold inner cross-validation for hyperparameter selection.",
+  "source": "OpenML_Dataset_ID_999",
+  "label": "clinical & healthcare",
+  "split_strategy": "cv",
+  "target_mapping": {
+    "0": "other source diagnoses (N)",
+    "1": "cochlear_age (P)"
+  },
+  "url": "https://www.openml.org/d/999"
+}