A multi-stage framework with prompt calibration, rule-based filtering, semantic checks, judge LLM review, and predictive validation enables trustworthy LLM extraction of substance use disorder diagnoses from nearly 920,000 clinical notes, achieving F1 of 0.80 and superior care-engagement prediction.
Predicting treatment retention in medication for opioid use disorder: A machine learning approach using nlp and llm-derived clinical features,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models
A multi-stage framework with prompt calibration, rule-based filtering, semantic checks, judge LLM review, and predictive validation enables trustworthy LLM extraction of substance use disorder diagnoses from nearly 920,000 clinical notes, achieving F1 of 0.80 and superior care-engagement prediction.