ArticleCritical care medicine2020
Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019.
Article in Critical care medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 4 of them syntheses that pooled it.
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Who cites it
115 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.Critical care explorations · 2025Pooled it
- External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- AEGIS: an operational infrastructure for post-market governance of adaptive medical AI under US and EU regulatory frameworks.NPJ digital medicine · 2026Article
- From PhysioNet to foundation models-a history and potential futures.Physiological measurement · 2026Article
- Privacy-Preserving Framework for Multi-Institutional Medical Time-Series Analysis via Homomorphic Encryption: Design and Development Study.JMIR formative research · 2026Article
- Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques.Scientific reports · 2026Article
- Federated TinyML and digital twin framework for secure and resilient IoMT-based ICU monitoring.Scientific reports · 2026Article
- Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset.BMJ health & care informatics · 2026Article
- Blood pressure response index trajectories identify distinct hemodynamic phenotypes and predict mortality in septic shock: a two-database retrospective cohort study.Critical care (London, England) · 2026Article
- Lightweight large language models for early sepsis prediction via a semantic abstraction rule engine.Scientific reports · 2026Article
- Stress Hyperglycemia Ratio and the Risk of Sepsis in Patients With Heart Failure: Retrospective Cohort Study From Medical Information Mart for Intensive Care-IV.JMIR medical informatics · 2026Article
- Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.BMC medical informatics and decision making · 2026Article
- Machine learning predicts sepsis deterioration trajectories.NPJ digital medicine · 2026Article
- The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study.Journal of medical Internet research · 2026Observational
- Responsible AI for Sepsis Prediction: Bridging the Gap Between Machine Learning Performance and Clinical Trust.Journal of clinical medicine · 2026Article
- Evaluating the Predictive Potential of an AI-Driven Deep Learning Model for Pneumonia-Associated Sepsis.Journal of clinical medicine · 2026Article
55 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
objectivesSepsis is a major public health concern with significant morbidity, mortality, and healthcare expenses. Early detection and antibiotic treatment of sepsis improve outcomes. However, although professional critical care societies have proposed new clinical criteria that aid sepsis recognition, the fundamental need for early detection and treatment remains unmet. In response, researchers have proposed algorithms for early sepsis detection, but directly comparing such methods has not been possible because of different patient cohorts, clinical variables and sepsis criteria, prediction tasks, evaluation metrics, and other differences. To address these issues, the PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data.
designParticipants submitted containerized algorithms to a cloud-based testing environment, where we graded entries for their binary classification performance using a novel clinical utility-based evaluation metric. We designed this scoring function specifically for the Challenge to reward algorithms for early predictions and penalize them for late or missed predictions and for false alarms.
settingICUs in three separate hospital systems. We shared data from two systems publicly and sequestered data from all three systems for scoring. PATIENTS: We sourced over 60,000 ICU patients with up to 40 clinical variables for each hour of a patient's ICU stay. We applied Sepsis-3 clinical criteria for sepsis onset.
interventionsNone. MEASUREMENTS AND MAIN
resultsA total of 104 groups from academia and industry participated, contributing 853 submissions. Furthermore, 90 abstracts based on Challenge entries were accepted for presentation at Computing in Cardiology.
conclusionsDiverse computational approaches predict the onset of sepsis several hours before clinical recognition, but generalizability to different hospital systems remains a challenge.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.