pith:6G62VCYO
OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
A two-stage training method that uses diagnosis hierarchies for contrastive learning and distills from notes improves vital-signs-only ICU risk prediction and label efficiency.
arxiv:2604.16878 v2 · 2026-04-18 · cs.LG
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Record completeness
Claims
Across multiple ICU prediction tasks on MIMIC, OC-Distill demonstrates improved label efficiency and achieves state-of-the-art performance among methods that use only vital signs at inference.
That the ICD diagnosis hierarchy supplies a clinically meaningful similarity metric that improves downstream risk prediction and that knowledge distilled from notes transfers usefully to a vital-signs-only encoder without introducing harmful biases.
OC-Distill combines ontology-aware contrastive pretraining with cross-modal distillation to improve ICU risk prediction performance and label efficiency while using only vital signs at inference.
Receipt and verification
| First computed | 2026-07-07T02:17:24.833354Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f1bdaa8b0e344eb2510e1fa73f4ed640a2a1a3030b63c2c8355e4ae29eeb5715
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6G62VCYOGRHLEUIOD6TT6TWWIC \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f1bdaa8b0e344eb2510e1fa73f4ed640a2a1a3030b63c2c8355e4ae29eeb5715
Canonical record JSON
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