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

• URL: http://arxiv.org/abs/2505.20323v1
• Date: Fri, 23 May 2025 18:01:09 GMT
• Title: PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus
• Authors: Shahriar Noroozizadeh, Sayantan Kumar, George H. Chen, Jeremy C. Weiss,
• Abstract summary: We present PMOA-TTS, the first openly available dataset of 124,699 annotated PubMed Open Access case reports.<n>Our approach combines filtering with Llama 3.3 to identify single-patient case reports, followed by prompt-driven extraction.<n>To assess timeline quality, we evaluate against a clinician-curated reference set using three metrics.
• Score: 9.924632472835551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding temporal dynamics in clinical narratives is essential for modeling patient trajectories, yet large-scale temporally annotated resources remain limited. We present PMOA-TTS, the first openly available dataset of 124,699 PubMed Open Access (PMOA) case reports, each converted into structured (event, time) timelines via a scalable LLM-based pipeline. Our approach combines heuristic filtering with Llama 3.3 to identify single-patient case reports, followed by prompt-driven extraction using Llama 3.3 and DeepSeek R1, resulting in over 5.6 million timestamped clinical events. To assess timeline quality, we evaluate against a clinician-curated reference set using three metrics: (i) event-level matching (80% match at a cosine similarity threshold of 0.1), (ii) temporal concordance (c-index > 0.90), and (iii) Area Under the Log-Time CDF (AULTC) for timestamp alignment. Corpus-level analysis shows wide diagnostic and demographic coverage. In a downstream survival prediction task, embeddings from extracted timelines achieve time-dependent concordance indices up to 0.82 $\pm$ 0.01, demonstrating the predictive value of temporally structured narratives. PMOA-TTS provides a scalable foundation for timeline extraction, temporal reasoning, and longitudinal modeling in biomedical NLP. The dataset is available at: https://huggingface.co/datasets/snoroozi/pmoa-tts .

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