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

arXiv: https://arxiv.org/abs/2604.06197 · HTML: https://mlsanity.com/paper/2604.06197/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?](https://mlsanity.com/paper/2411.04118/) (2024)
- [A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports](https://mlsanity.com/paper/2504.12350/) (2025)
- [Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare](https://mlsanity.com/paper/2504.10340/) (2025)
- [Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis](https://mlsanity.com/paper/2504.12326/) (2025)
- [PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus](https://mlsanity.com/paper/2505.20323/) (2025)

## Built on by (most cited first, 2 of 2)

- [Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment](https://mlsanity.com/paper/2605.15168/) (2026)
- [Grounded Adjudication of Variations across Extracted TimeLines (GAVEL): Comparing Clinical Timelines Against Their Case Reports](https://mlsanity.com/paper/2609.13475/) (2026)

## Abstract (arXiv)

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.