Predicting treatment retention in medication for opioid use disorder: a machine learning approach using NLP and LLM-derived clinical features.

ABSTRACT:

Objective

Building upon our previous work on predicting treatment retention in medications for opioid use disorder, we aimed to improve 6-month retention prediction in buprenorphine-naloxone (BUP-NAL) therapy by incorporating features derived from large language models (LLMs) applied to unstructured clinical notes.

Materials and methods

We used de-identified electronic health record (EHR) data from Stanford Health Care (STARR) for model development and internal validation, and the NeuroBlu behavioral health database for external validation. Structured features were supplemented with 13 clinical and psychosocial features extracted from free-text notes using the CLinical Entity Augmented Retrieval pipeline, which combines named entity recognition with LLM-based classification

SUBMITTER: Nateghi Haredasht F 

PROVIDER: S-EPMC12646374 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature