Dmitry43243242/icd10-ru-subgroup-i

ICD-10 subgroup classifier — group I (Russian)

Multi-label classifier over 3-character ICD-10 subgroups inside chapter I.
Fine-tuned from 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_I.csv
• SHA-256: 9f277d2bf673422bf44bd7a445079b896f0ba9e66384859d719c13508bc8c9eb
• Produced by ml/build_subgroup_datasets.ipynb (iterative multi-label stratification by parse_id).
• Splits: train=740 · val=156 · test=161
• Labels: 63 (ordered, includes I_OTHER for rare codes collapsed during dataset build).

Metrics (test split)

metric	value
macro_f1	0.5897
micro_f1	0.5959
weighted_f1	0.6320
subset_accuracy	0.2547
hit@1	0.7329
hit@3	0.8634
recall@3	0.8428
mrr	0.8141

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

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

repo = "Dmitry43243242/icd10-ru-subgroup-i"
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).