ArticleScientific reports2023
Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Evaluating the generalisability of LiSep LSTM for early prediction of septic shock across US and european cohorts.Scientific reports · 2026Article
- Performance of a Sepsis Prediction Model Across Different Sepsis Definitions.JAMA network open · 2026Article
- Development of a machine learning-based sepsis prediction model for real-world clinical settings in South Korea: a single-center retrospective study.Journal of Korean biological nursing science · 2026Article
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- Artificial intelligence in acute and critical care: current challenges and strategic solutions.Frontiers in public health · 2026Review
- Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.Annals of medicine · 2025Article
- Leveraging AI to Enhance Electronic Health Records.EJIFCC · 2025Review
- GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction.BMC medical informatics and decision making · 2025Article
- Improving Sepsis Prediction in the ICU with Explainable Artificial Intelligence: The Promise of Bayesian Networks.Journal of clinical medicine · 2025Article
- The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence.Journal of internal medicine · 2025Review
- Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.Scientific reports · 2025Article
- Artificial Intelligence in Sepsis Management: An Overview for Clinicians.Journal of clinical medicine · 2025Review
- Early Warning Systems for Critical Illness Outside the Intensive Care Unit.Critical care clinics · 2024Review
- [Data-driven intensive care: a lack of comprehensive datasets].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- AI integration in sepsis care: a step towards improved health and quality of life outcomes.Annals of medicine and surgery (2012) · 2024Article
- Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers.European journal of medical research · 2024Article
- Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.Frontiers in medicine · 2024Review
- Analysis of prognostic factors in patients with emergency sepsis.World journal of clinical cases · 2023Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.
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