# Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis

arXiv: https://arxiv.org/abs/2504.12326 · HTML: https://mlsanity.com/paper/2504.12326/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?](https://mlsanity.com/paper/2411.04118/) (2024)
- [Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression](https://mlsanity.com/paper/2312.05933/) (2023)

## Built on by (most cited first, 5 of 5)

- [Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare](https://mlsanity.com/paper/2504.10340/) (2025)
- [PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus](https://mlsanity.com/paper/2505.20323/) (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)
- [Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment](https://mlsanity.com/paper/2605.15168/) (2026)
- [Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication](https://mlsanity.com/paper/2609.13062/) (2026)

## Abstract (arXiv)

Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e., timestamped after the encounter. Complementary structured data streams become available sooner but suffer from incompleteness. To train models and algorithms on more complete and temporally fine-grained data, we construct a pipeline to phenotype, extract, and annotate time-localized findings within case reports using large language models. We apply our pipeline to generate an open-access textual time series corpus for Sepsis-3 comprising 2,139 case reports from the PubMed-Open Access (PMOA) Subset. To validate our system, we apply it to PMOA and timeline annotations from i2b2/MIMIC-IV and compare the results to physician-expert annotations. We show high recovery rates of clinical findings (event match rates: GPT-5--0.93, Llama 3.3 70B Instruct--0.76) and strong temporal ordering (concordance: GPT-5--0.965, Llama 3.3 70B Instruct--0.908). Our work characterizes the ability of LLMs to time-localize clinical findings in text, illustrating the limitations of LLM use for temporal reconstruction and providing several potential avenues of improvement via multimodal integration.