PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus

TL;DR

This paper introduces PMOA-TTS, a large-scale corpus of structured temporal data from PubMed case reports, enabling advanced research in temporal reasoning and patient trajectory modeling using NLP.

Contribution

The creation of PMOA-TTS, a comprehensive, annotated corpus of over 5.6 million timestamped events from clinical narratives, with validation and benchmarking tools included.

Findings

High-quality temporal annotations validated against clinician-curated data

Benchmarking of prompting and model choices for timeline extraction

Open access to data and tools for further research

Abstract

Clinical narratives encode temporal dynamics essential for modeling patient trajectories, yet large-scale temporally annotated resources are scarce. We introduce PMOA-TTS, a corpus of 124,699 single-patient PubMed Open Access case reports converted into structured textual timelines of (event, time) pairs using a scalable large-language-model pipeline (Llama 3.3 70B and DeepSeek-R1). The corpus comprises over 5.6 million timestamped events, alongside extracted demographics and diagnoses. Technical validation uses a clinician-curated gold set and three measures: semantic event matching, temporal concordance (c-index), and alignment error summarized with Area Under the Log-Time CDF (AULTC). We benchmark alternative prompting and model choices and provide documentation to support reproduction. PMOA-TTS enables research on timeline extraction, temporal reasoning, survival modeling and event forecasting from narrative text, and offers broad diagnostic and demographic coverage. Data and code are openly available in public repositories.