Dmitry43243242
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icd10-ru-subgroup-p

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 ---
+language:
+- ru
+license: apache-2.0
+tags:
+- medical
+- icd-10
+- multi-label-classification
+- russian
+base_model: ai-forever/ruBert-base
+pipeline_tag: text-classification
 ---
+# ICD-10 subgroup classifier — group P (Russian)
+Multi-label classifier over 3-character ICD-10 subgroups inside chapter **P**.
+Fine-tuned from [`ai-forever/ruBert-base`](https://huggingface.co/ai-forever/ruBert-base) on Russian clinical text.
+## Intended use / Назначение
+- **EN:** Decision-support signal for suggesting candidate ICD-10 subgroups from Russian clinical notes. **Not** a substitute for clinician judgment; not validated for autonomous diagnosis.
+- **RU:** Вспомогательный сигнал для предложения кандидатных 3-символьных кодов МКБ-10 по русскому клиническому тексту. **Не заменяет** врача и не предназначен для автономных клинических решений.
+## Training data / Обучающие данные
+- Source CSV: `datasets/subgroups/group_P.csv`
+- SHA-256: `eaa1a9f6e52dba1c8167c8a5c40d1d455bc2e6072842e6cb6bc3c1c09fd67d4c`
+- Produced by `ml/build_subgroup_datasets.ipynb` (iterative multi-label stratification by `parse_id`).
+- Splits: train=159 · val=39 · test=38
+- Labels: 25 (ordered, includes `P_OTHER` for rare codes collapsed during dataset build).
+## Metrics (test split)
+| metric | value |
+|---|---|
+| macro_f1 | 0.7660 |
+| micro_f1 | 0.7414 |
+| weighted_f1 | 0.7773 |
+| subset_accuracy | 0.6053 |
+| hit@1 | 0.9211 |
+| hit@3 | 0.9474 |
+| recall@3 | 0.9474 |
+| mrr | 0.9430 |
+Full per-label breakdown in `metrics.json`.
+## Limitations / Ограничения
+- Russian only; heavy reliance on clinical abbreviations (АД, ТТГ, УЗИ, etc.).
+- Training text had PII redacted (`*ДАТА*`, `*ГОРОД*`, ...); model may behave differently on non-redacted input.
+- Small chapters (train rows < 250) were trained with heavy regularization; some labels may have low support.
+- Rare labels without positives in train are kept in the label map (see `label_map.json → rare_label_ids`) for interface stability but will effectively never fire.
+## Inference
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+repo = "Dmitry43243242/icd10-ru-subgroup-p"
+tok = AutoTokenizer.from_pretrained(repo)
+mdl = AutoModelForSequenceClassification.from_pretrained(repo)
+mdl.eval()
+text = "жалобы пациента..."
+inp = tok(text, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    probs = torch.sigmoid(mdl(**inp).logits)[0]
+preds = [mdl.config.id2label[i] for i, p in enumerate(probs.tolist()) if p >= 0.5]
+top3 = sorted(
+    [(mdl.config.id2label[i], p) for i, p in enumerate(probs.tolist())],
+    key=lambda x: -x[1],
+)[:3]
+print(preds, top3)
+```
+## Citation / Ссылка
+Built as part of the `ai-app` ICD-10 classification pipeline. Upstream model: `ai-forever/ruBert-base` (ai-forever).