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

arXiv:2604.06197v1 Announce Type: new 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

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Sayantan Kumar, Jeremy C. Weiss · · 1 min read · 12 views
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arXiv:2604.06197v1 Announce Type: new 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.

Executive Summary

This article introduces a novel textual time-series corpus derived from 136 PubMed Open Access case reports concerning GLP-1 receptor agonist (GLP-1RA) use in Type 2 diabetes. The authors developed a methodology to extract and temporally align clinical events using Large Language Models (LLMs), specifically demonstrating high performance with GPT-5 in recovering events and their temporal sequencing. This innovative approach transforms unstructured narrative into structured, longitudinal data suitable for quantitative analysis. As a proof-of-concept, the study performed time-to-event analyses, suggesting a reduced risk of respiratory sequelae in GLP-1RA users, aligning with existing literature. The work holds significant promise for leveraging vast amounts of unstructured clinical text for real-world evidence generation.

Key Points

  • ▸ Development of a novel textual time-series corpus from GLP-1RA case reports.
  • ▸ Evaluation of LLMs for automated extraction of clinical events and their temporal sequencing.
  • ▸ GPT-5 demonstrated high performance in event coverage (0.871) and temporal sequencing (0.843).
  • ▸ Application of the structured data to time-to-event analysis, indicating lower respiratory sequelae risk in GLP-1RA users.
  • ▸ The methodology transforms unstructured clinical narratives into reusable longitudinal data.

Merits

Innovative Corpus Creation

The creation of a specialized textual time-series corpus from unstructured case reports is a significant methodological advancement, transforming qualitative data into quantitative.

Robust LLM Performance

Demonstrating high performance of GPT-5 in both event extraction and temporal ordering against gold-standard annotations validates the utility of advanced LLMs in this domain.

Real-World Evidence Generation

The approach offers a scalable method to extract granular, longitudinal real-world evidence from a vast, underutilized resource of clinical narratives.

Proof-of-Concept Utility

The downstream time-to-event analysis, yielding a finding consistent with prior research, effectively showcases the immediate utility and potential impact of the structured data.

Demerits

Limited Corpus Size

A corpus of 136 case reports, while a strong start, may not fully capture the breadth of clinical variability and rare events necessary for robust epidemiological conclusions.

LLM Generalizability

The specific LLM (GPT-5) used might not be universally accessible or perform identically on different datasets or clinical specialties, raising questions about generalizability without further validation.

Bias in Case Reports

Case reports inherently suffer from selection bias (often highlighting unusual or notable cases) and reporting bias, which could influence the derived 'real-world' insights.

Inter-annotator Variability

While gold standards were used, the article does not detail the inter-annotator agreement for the expert annotations, which is crucial for assessing the reliability of the 'true' temporal sequences.

Expert Commentary

This paper represents a significant methodological stride in bridging the chasm between the richness of narrative clinical data and the rigor of quantitative analysis. The creation of a 'textual time-series corpus' is not merely an incremental improvement but a conceptual leap, demonstrating how advanced LLMs can unlock previously inaccessible longitudinal insights from a ubiquitous source: case reports. The high performance of GPT-5 in temporal phenotyping is particularly noteworthy, suggesting that the era of manual chart abstraction for research purposes may be nearing its end for certain applications. While the initial corpus size is modest, the methodology's scalability is its true power. The downstream application, confirming a known association (GLP-1RA and respiratory outcomes), serves as an elegant validation, lending credibility to the approach. Future work must rigorously address potential biases inherent in case report literature and the generalizability of LLM performance across diverse clinical contexts and languages. Nevertheless, this work lays a robust foundation for a new paradigm in real-world evidence generation.

Recommendations

  • ✓ Expand the corpus size and diversity (e.g., across different drug classes, disease areas, and geographical regions) to validate generalizability and enhance statistical power for novel discoveries.
  • ✓ Conduct a thorough bias analysis of the case report selection and reporting, and develop methods to mitigate these biases in the extracted data.
  • ✓ Explore the performance of other LLMs (both proprietary and open-source) and fine-tuning strategies to assess robustness and accessibility of the methodology.
  • ✓ Provide comprehensive details on the gold-standard annotation process, including inter-annotator agreement metrics, to bolster confidence in the evaluation benchmarks.
  • ✓ Investigate the interpretability of LLM decisions in temporal extraction, particularly when faced with ambiguous or contradictory temporal cues in the text.

Sources

Original: arXiv - cs.CL

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