ArticleNature communications2021
Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers, 9 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
183 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled 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
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study.Frontiers in medicine · 2025Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Prognostic value of serum phosphate levels in sepsis: a systematic review and meta-analysis.PeerJ · 2023Pooled 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
- Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.Internal and emergency medicine · 2024Trial
- Earlier ICU Transfer after CONCERN Early Warning System Score Escalation Reduced Sepsis-related Mortality: Results from a Multi-site Pragmatic Cluster Randomized Controlled Trial.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Trial
- Algorithm-assisted personalized risk communication to encourage flu vaccination in the USA: three randomized field trials.Nature human behaviour · 2026Article
- Automating chart review for identification of infected patients using natural language processing.JAMIA open · 2026Article
- Evaluating the generalisability of LiSep LSTM for early prediction of septic shock across US and european cohorts.Scientific reports · 2026Article
- Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care.World journal of critical care medicine · 2026Review
- Subgroup Differences in Agreement Between an Algorithm Guided Large Language Model and Routine Emergency Department Triage.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.Infection · 2026Article
- The Designated Institutional Official: An Essential GME Role, But What Do They Actually Do?Journal of general internal medicine · 2026Article
- Deep learning analysis of 12-lead electrocardiograms for bloodstream infection prediction: a multi-center validation study.BMC medical informatics and decision making · 2026Article
123 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm's potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm's accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.
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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.