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.

The authors have declared no competing interest.

This research was supported by the Intramural Research Program of the National Institutes of Health (NIH) and utilized the computational resources of the NIH HPC Biowulf cluster.

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We use case reports from the PubMed Open Access (PMOA) repository which are publicly available