Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling

• URL: http://arxiv.org/abs/2604.06197v1
• Date: Thu, 12 Mar 2026 20:45:06 GMT
• Title: Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling
• Abstract summary: We developed a time-series corpus of case reports involving glucagon-like peptide 1 receptor agonists.<n>We evaluated automated LLM timeline extraction against gold-standard timelines annotated by clinical domain experts.<n>The best-performing LLM produced high event coverage, reliable temporal sequencing across symptoms, diagnoses, treatments, laboratory tests, and outcomes.
• Score: 3.6611037112655818
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Type 2 diabetes case reports describe complex clinical courses, but their timelines are often expressed in language that is difficult to reuse in longitudinal modeling. To address this gap, we developed a textual time-series corpus of 136 PubMed Open Access single-patient case reports involving glucagon-like peptide 1 receptor agonists, with clinical events associated with their most probable reference times. We evaluated automated LLM timeline extraction against gold-standard timelines annotated by clinical domain experts, assessing how well systems recovered clinical events and their timings. The best-performing LLM produced high event coverage (GPT5; 0.871) and reliable temporal sequencing across symptoms (GPT5; 0.843), diagnoses, treatments, laboratory tests, and outcomes. As a downstream demonstration, time-to-event analyses in diabetes suggested lower risk of respiratory sequelae among GLP-1 users versus non-users (HR=0.259, p<0.05), consistent with prior reports of improved respiratory outcomes. Temporal annotations and code will be released upon acceptance.

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