Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling
Summary: arXiv:2604.06197v1 Announce Type: cross
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 (GLP-1RAs), with clinical events associated with their most probable reference times.
Introduction
The management of Type 2 diabetes often requires a nuanced understanding of the individual patient’s clinical journey. Case reports serve as valuable resources, but extracting useful information from these documents has proven challenging. Traditional methods of longitudinal modeling often struggle with the variability in how clinical events are described and sequenced. This study aims to leverage large language models (LLMs) to create a structured approach to analyzing these case reports.
Methodology
In our research, we compiled a textual time-series corpus consisting of 136 case reports from PubMed Open Access, focusing specifically on GLP-1RAs. Each report was annotated with clinical events and their corresponding reference times, creating a gold-standard timeline for evaluation.
We employed several automated LLMs to extract timelines from the case reports. These models were compared against the gold-standard timelines annotated by clinical domain experts. The assessment focused on two primary metrics:
- Event Coverage: The proportion of clinical events accurately extracted by the LLM.
- Temporal Sequencing: The accuracy of the chronological order in which events were reported.
Results
The results of our evaluation revealed that the best-performing LLM, GPT5, demonstrated impressive capabilities in both metrics. Specifically, it achieved:
- High event coverage at 0.871, indicating that a significant majority of clinical events were accurately identified.
- Reliable temporal sequencing across various categories, including symptoms, diagnoses, treatments, laboratory tests, and outcomes, with a score of 0.843.
Discussion
The findings underscore the potential of LLMs in transforming how we analyze case reports in clinical settings. By effectively extracting timelines and events, these models can facilitate a better understanding of the complexities involved in managing Type 2 diabetes. Furthermore, as a downstream demonstration of our methodology, we conducted time-to-event analyses which suggested a lower risk of respiratory sequelae among GLP-1 users compared to non-users, with a hazard ratio of 0.259 (p < 0.05).
Conclusion
This study illustrates the utility of LLMs in extracting meaningful insights from clinical case reports. Our approach not only enhances the usability of these documents for longitudinal modeling but also opens avenues for further research into the impacts of GLP-1RAs on patient outcomes. As we continue to refine these techniques, the integration of LLMs into clinical analytics holds promise for improving patient care and health outcomes in the realm of diabetes management.