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Research questionHow can automated NLP reliably extract colorectal-cancer risk signals from younger adults’ clinical notes?Structured encounter data often omit symptom duration, context, and family history, while these details may be important for assessing colorectal-cancer risk in younger adults. The challenge is identifying clinically grounded risk information without generating unnecessary positive findings.
AI
Health
Natural Language Processing
Research Paper
Latest papersRecent research connected to this question, newest first.VERGE: Verification-Enhanced Refinement for Grounded Extraction of Early-Onset Colorectal Cancer Symptoms in Clinical NotesThe evidence concerns automated extraction from clinician-authored notes using 4,033 clinician-labeled note-finding pairs. The study reports comparisons with a single-agent system, a rule-based clinical language-processing baseline, and another language model; it does not establish effects on patient outcomes or follow-up decisions.research paper · Sep 3, 2026
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