# A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports

arXiv: https://arxiv.org/abs/2504.12350 · HTML: https://mlsanity.com/paper/2504.12350/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Zero-shot Temporal Relation Extraction with ChatGPT](https://mlsanity.com/paper/2304.05454/) (2023)
- [An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining](https://mlsanity.com/paper/2005.02799/) (2020)
- [PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge](https://mlsanity.com/paper/2401.11048/) (2024)
- [Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data](https://mlsanity.com/paper/2401.06774/) (2023)
- [Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency](https://mlsanity.com/paper/2406.11486/) (2024)

## Built on by (most cited first, 10 of 10)

- [PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus](https://mlsanity.com/paper/2505.20323/) (2025)
- [Metric-Fair Prompting: Treating Similar Samples Similarly](https://mlsanity.com/paper/2512.07608/) (2025)
- [Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2](https://mlsanity.com/paper/2506.22444/) (2025)
- [Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling](https://mlsanity.com/paper/2604.06197/) (2026)
- [Immunological Density Shapes Recovery Trajectories in Long COVID](https://mlsanity.com/paper/2601.07854/) (2026)
- [C-kNN-LSH: A Nearest-Neighbor Algorithm for Sequential Counterfactual Inference](https://mlsanity.com/paper/2602.02371/) (2026)
- [Trajectory-Aware Retrieval Agents for Temporal Decision- Making](https://mlsanity.com/paper/2607.21625/) (2026)
- [Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication](https://mlsanity.com/paper/2609.13062/) (2026)
- [Grounded Adjudication of Variations across Extracted TimeLines (GAVEL): Comparing Clinical Timelines Against Their Case Reports](https://mlsanity.com/paper/2609.13475/) (2026)
- [CliniCIRCA: A Modular LLM Framework for Constructing Longitudinal Mental Health Patient Journeys from Raw EHR Narratives](https://mlsanity.com/paper/2609.19585/) (2026)

## Abstract (arXiv)

Timing of clinical events is central to characterization of patient trajectories, enabling analyses such as process tracing, forecasting, and causal reasoning. However, structured electronic health records capture few data elements critical to these tasks, while clinical reports lack temporal localization of events in structured form. We present a system that transforms case reports into textual time series-structured pairs of textual events and timestamps. We contrast manual and large language model (LLM) annotations (n=320 and n=390 respectively) of ten randomly-sampled PubMed open-access (PMOA) case reports (N=152,974) and assess inter-LLM agreement (n=3,103; N=93). We find that the LLM models have moderate event recall(O1-preview: 0.80) but high temporal concordance among identified events (O1-preview: 0.95). By establishing the task, annotation, and assessment systems, and by demonstrating high concordance, this work may serve as a benchmark for leveraging the PMOA corpus for temporal analytics.