A Rubric-Guided Large Language Model Solution for Opioid Use Disorder Computable Phenotyping
Opioid use disorder (OUD) remains a public health crisis in the United States, yet it is difficult to identify from electronic health records (EHRs) because missing diagnosis codes and supporting evidence are buried in clinical narratives. Accurate OUD identification is critical to support interventions and improve health outcomes. This study developed a rubric-guided large language model (LLM) that incorporated Optimization by PROmpting (OPRO) for OUD computable phenotyping (CP). The framework used an 18-item, expert-identified rubric to instruct LLMs to automatically extract critical text with supporting evidence to determine OUD flags. Two UF Health physicians (GMR and WMG) chart-revie...
| Authors | Mengxian Lyu, Paredes Pardo, Cheng Peng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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