{"slug": "verge-verification-enhanced-refinement-for-grounded-extraction-of-early-onset-in", "title": "VERGE: Verification-Enhanced Refinement for Grounded Extraction of Early-Onset Colorectal Cancer Symptoms in Clinical Notes", "summary": "Researchers developed VERGE, an agentic workflow using retrieval-augmented generation and a bounded verification-refinement cycle, to extract red-flag symptoms and family-history risk status for early-onset colorectal cancer from clinical notes. In an evaluation on 4,033 clinician-labeled note-finding pairs, VERGE improved precision from 0.764 to 0.849 and Matthews correlation coefficient from 0.681 to 0.730 compared with a single-agent baseline, while requiring human review for only 1.5% of claims.", "body_md": "arXiv:2609.04366v1 Announce Type: new \nAbstract: Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have no evidence-based guidelines for follow-up testing, and structured encounter data do not capture the detail needed to support early detection and inform follow-up, including symptom duration, context, and fam- ily history, an established colorectal-cancer risk factor. This study aimed to develop and evaluate an automated method for extracting six red-flag symptoms and family-history risk status from free-text clinical notes. We developed VERGE, an agentic workflow in which an initial label and evidence are proposed using retrieval-augmented generation, then passed through a bounded verification- refinement cycle that checks textual grounding and clinical validity, corrects and rechecks a claim until resolved or a limit is reached, and escalates unresolved claims for human review. VERGE was evaluated on 4,033 clinician-labeled note-finding pairs against a single-agent baseline, a rule-based clinical language-processing baseline, and an alternative underlying language model. Compared with the single-agent baseline, VERGE reduced false positive find- ings, improving precision from 0.764 to 0.849 and MCC from 0.681 to 0.730, a balanced gain across the precision-recall trade-off, and resolved most flagged errors autonomously, with human review required for only 1.5 percent of claims. These results indicate that a bounded, verification-based workflow can reduce unnecessary positive findings without sacrificing the ability to detect true ones. This approach offers a path toward more reliable and trustworthy clinical language-processing tools to support colorectal cancer risk assessment in younger patients.", "url": "https://wpnews.pro/news/verge-verification-enhanced-refinement-for-grounded-extraction-of-early-onset-in", "canonical_source": "https://arxiv.org/abs/2609.04366", "published_at": "2026-09-07 04:00:00+00:00", "updated_at": "2026-09-07 04:25:28.574752+00:00", "lang": "en", "topics": ["artificial-intelligence", "natural-language-processing", "ai-research", "ai-agents"], "entities": ["VERGE"], "alternates": {"html": "https://wpnews.pro/news/verge-verification-enhanced-refinement-for-grounded-extraction-of-early-onset-in", "markdown": "https://wpnews.pro/news/verge-verification-enhanced-refinement-for-grounded-extraction-of-early-onset-in.md", "text": "https://wpnews.pro/news/verge-verification-enhanced-refinement-for-grounded-extraction-of-early-onset-in.txt", "jsonld": "https://wpnews.pro/news/verge-verification-enhanced-refinement-for-grounded-extraction-of-early-onset-in.jsonld"}}