Observational studyCritical care medicine2018
An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.
Observational study in Critical care medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06245694 (Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits), which is not on this map. Cited by 343 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits
Who cites it
343 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- The Role of the Cardiothoracic Surgeon in the Age of AI-Are the Robots Going to Take Our Jobs?Medical sciences (Basel, Switzerland) · 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
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- Machine Learning-Based Prediction of Culture-Confirmed Neonatal Sepsis in a Tertiary Neonatal Intensive Care Unit: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Artificial intelligence in emergency medicine: from algorithmic performance to real-world clinical integration - understanding the implementation gap.International journal of emergency medicine · 2026Review
- Physiological Data Integration and Predictive Modeling in Intensive Care.Life (Basel, Switzerland) · 2026Review
- Development and multi-center validation of a machine learning‑based prediction model for mortality in tumor-related sepsis.BMC infectious diseases · 2026Article
- Interpretable machine learning models for predicting perioperative myocardial injury in non-cardiac surgery.European heart journal. Digital health · 2026Article
- Real-Time Artificial Intelligence for Early Sepsis Prediction Using Dynamic Clinical Data: A Systematic Review.Cureus · 2026Review
- Article
- Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Machine learning prediction of PICC-associated thrombotic complications in critically ill patients.BMC medical informatics and decision making · 2026Article
- Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study.World journal of emergency surgery : WJES · 2026Article
- Application of machine learning to predict bronchiolitis severity in children: a single-centre retrospective cohort study.European journal of pediatrics · 2026Article
- Association between systemic immune-inflammation index and ICU-acquired infection in critically Ill patients without infection: a competing risks analysis from a retrospective cohort.BMC infectious diseases · 2026Article
- Development Process of a Clinical Decision Support System for Empiric Antibiotic Therapies in Patients With Sepsis: Case Study.JMIR medical informatics · 2026Article
283 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
6 authors.
Funding
Abstract
objectivesSepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis.
designObservational cohort study.
settingAcademic medical center from January 2013 to December 2015. PATIENTS: Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively.
interventionsNone. MEASUREMENTS AND MAIN
resultsHigh-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable.
conclusionsUsing data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.
Indexed as
Identifiers
What OpenQuestion holds
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.