ArticleNPJ digital medicine2025
Development and prospective implementation of a large language model based system for early sepsis prediction.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patients.Cell reports. Medicine · 2026Article
- Improving sepsis best practice utility and clinical acceptance using an LLM-enhanced prediction system.npj health systems · 2026Article
- DETERIO-LLM: enhancing traditional deterioration risk scores with clinical context and advanced reasoning.JAMIA open · 2026Article
- Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.Nature communications · 2026Article
- Preoperative prediction of postoperative SIRS and sepsis after ureteroscopic lithotripsy: discriminative performance of large language models.Urolithiasis · 2026Article
- Integrating Precision Sepsis Risk Stratification Into Systems-Based Antimicrobial Stewardship.Open forum infectious diseases · 2026Article
- Generative AI in simulation debriefings: an exploratory study using the Team-FIRST framework and qualitative feedback from simulation experts and learners.Advances in simulation (London, England) · 2026Article
- Hospital-Wide Sepsis Detection: A Machine Learning Model Based on Prospectively Expert-Validated Cohort.Journal of clinical medicine · 2026Article
- Rethinking sepsis prediction in the era of large language models.npj health systems · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Artificial Intelligence in Predictive Healthcare: A Systematic Review.Journal of clinical medicine · 2025Review
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
- Achieving a rapid and continuous learning health system requires socio-technical harmonization of research and operational IT.npj health systems · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve patient outcomes, but existing computational models relying on structured electronic health record data often miss contextual information from unstructured clinical notes. This study introduces COMPOSER-LLM, an open-source large language model (LLM) integrated with the COMPOSER model to enhance early sepsis prediction. For high-uncertainty predictions, the LLM extracts additional context to assess sepsis-mimics, improving accuracy. Evaluated on 2500 patient encounters, COMPOSER-LLM achieved a sensitivity of 72.1%, positive predictive value of 52.9%, F-1 score of 61.0%, and 0.0087 false alarms per patient hour, outperforming the standalone COMPOSER model. Prospective validation yielded similar results. Manual chart review found 62% of false positives had bacterial infections, demonstrating potential clinical utility. Our findings suggest that integrating LLMs with traditional models can enhance predictive performance by leveraging unstructured data, representing a significant advance in healthcare analytics.
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Registered trials
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