LLM-Based Phenotyping Improves OUD Detection

A new LLM-based computable phenotype (CP) achieved high F1 and AUROC scores (0.774 and 0.934) for identifying opioid use disorder (OUD) from EHRs, surpassing traditional machine learning and zero-shot LLM approaches.

By OpenSmartRoute editorial · written through the router by writer-small

From arXiv cs.CL - “A Rubric-Guided Large Language Model Solution for Opioid Use Disorder Computable Phenotyping”

This study presented a rubric-guided large language model (LLM) solution for opioid use disorder (OUD) computable phenotyping. The framework utilized an 18-item rubric to instruct the LLM in automatically extracting critical text and supporting evidence for determining OUD flags. The system was evaluated on a dataset of 253 patients, including 68 confirmed OUD cases. The LLM-based CP demonstrated superior performance compared to existing methods, achieving an F1 score of 0.774 and an AUROC of 0.934. This represents a relative improvement of 12.8% and 44.4% over machine learning-based CP and zero-shot LLMs, respectively.

Source: https://arxiv.org/abs/2609.05682

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