כתבה
arXiv cs.LG ·
TRIAGE: ניבוי סיכונים ברפואה
TRIAGE: Dialectical LLM Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series
TRIAGE הוא כלי לניבוי סיכונים רפואיים. הוא משתמש במודל LLM כדי לספק ניבויים מדויקים וניתנים להבנה. TRIAGE משפר את דיוק הניבוי ב-17% ומקטין את שגיאת הכיול ב-82.8%
תקציר מקורי באנגליתarXiv:2606.09030v2 Announce Type: replace Abstract: Clinical early warning systems built on irregularly sampled medical time series (ISMTS) from electronic health records must deliver continuous risk scores for patient triage as well as interpretable rationales that clinicians can verify. Large language models (LLMs) are uniquely positioned for both, deriving risk from their output probabilities and rationales from their medical knowledge. However, we find that conventional LLM reasoning collapses graded risk into overconfident predictions and thereby undermines the cross-patient comparability on which triage depends. We refer to this failure mode as risk polarization and identify two underlying behaviors: early commitment to a single outcome, and one-sided reasoning that focuses only on t
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arxiv.org
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