An interpretable machine learning model for accurate prediction of sepsis in the ICU

Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D.; Clifford, Gari D.; Buchman, Timothy G.

doi:10.1097/ccm.0000000000002936

Cited by 832Sepsis Diagnosis and TreatmentMachine Learning in HealthcareAcute Kidney Injury Research

An interpretable machine learning model for accurate prediction of sepsis in the ICU

Open access

Nemati, Shamim, Holder, Andre, Razmi, Fereshteh, Stanley, Matthew D., Clifford, Gari D., Buchman, Timothy G.·2018·Critical Care Medicine

DOI 10.1097/ccm.0000000000002936PMC PMC5851825

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Abstract

Objectives: Sepsis 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. Design: Observational cohort study. Setting: Academic 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. Interventions: None. Measurements and Main Results: High-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. Conclusions: Using 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.

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ML models can analyse a vast number of variables and their interactions, including non-linear relationships, and adapt to different patient populations and settings.
“In contrast, ML models can analyse a vast number of variables and their interactions, including non-linear relationships, and adapt to different patient populations and settings.19”
From Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning study (2025) · cites this paper as [19]
The Discussion says machine learning is useful for many variables and complex, nonlinear relationships, and the Methods report 65 input features. The Discussion also reports validation at a separate academic center, but the paper does not establish that ML models adapt to different patient populations and settings generally. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
AI models that use EHR, laboratory, and physiological data predict outcomes such as acute kidney injury and sepsis or critical-care deterioration.
“Models that use EHR, laboratory, and physiological data predict outcomes such as acute kidney injury [14] and sepsis or critical-care deterioration [15,16].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [16]
The Discussion reports that a model combining EMR data with heart-rate and blood-pressure time series predicted sepsis four hours in advance (AUROC 0.85); it also identifies lactate as an EMR input. The Discussion says prediction of clinical decompensation regardless of cause is only a possibility requiring further research, so this source does not establish the critical-care-deterioration part. Acute kidney injury has its own citation in the citing sentence and is not attributed to this paper. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Early warning systems are increasingly used in critical care to predict sepsis, acute deterioration, and the need for intensive care escalation.
“Early warning systems are increasingly used in critical care to predict sepsis, acute deterioration, and the need for intensive care escalation [15,16].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [16]
The Introduction and Conclusion (pages unavailable) describe clinical decision-support tools and this study’s model for predicting sepsis in ICU patients. The Discussion says prediction of other forms of physiologic decompensation is only a potential application requiring further research. Our copy does not establish increasing use of these systems or prediction of a need for intensive-care escalation. (LLM, conf=0.85)
Experimental — accuracy not yet measured
AI models analyse dynamic clinical data, including vital signs and laboratory results, to generate alerts before clinical recognition, enabling earlier intervention.
“AI models analyse dynamic clinical data, including vital signs and laboratory results, to generate alerts before clinical recognition, enabling earlier intervention [16].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [16]
The Measurements and Main Results and Conclusion report that AISE combined high-resolution vital signs with EMR data to predict sepsis 4–12 hours before clinical recognition. The Discussion proposes clinician alerts and earlier action, but says prospective validation and an intervention trial are still needed; it does not show that the model generated clinical alerts or enabled earlier intervention. The copy also does not establish laboratory results as a specific input to AISE. (LLM, conf=0.88)
Experimental — accuracy not yet measured
A deep learning approach using recurrent neural networks predicted sepsis onset hours in advance within ICU cohorts, demonstrating that model-driven alerts could theoretically increase lead time for intervention.
“A deep learning approach using recurrent neural networks predicted sepsis onset hours in advance within ICU cohorts, demonstrating that model-driven alerts could theoretically increase lead time for intervention [50].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [50]
The Abstract Conclusion reports that AISE predicted sepsis onset in ICU patients 4–12 hours before clinical recognition, supporting the potential for earlier alerts. But the Methods section identifies the model as a modified Weibull-Cox proportional hazards model, not a deep-learning recurrent neural network. The paper also says prospective study is needed to establish clinical utility. (LLM, conf=0.91)
Experimental — accuracy not yet measured

Numerous models were tested on the freely accessible MIMIC database.
“numerous models were tested on the freely accessible MIMIC database [27, 33, 52–59]”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [55]
Numerous studies have indicated that ML algorithms outperform SIRS, SOFA, qSOFA, and MEWS scores.
“Numerous studies have indicated that ML algorithms outperform SIRS, SOFA, qSOFA, and MEWS scores.19,27,28”
From Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning study (2025) · cites this paper as [19]
The Results report strong sepsis-prediction performance for the paper’s machine-learning model, and the Discussion compares it with another machine-learning algorithm. Neither passage compares machine learning with SIRS, SOFA, qSOFA, or MEWS scores; searches for those score comparisons found no supporting passage in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Digital pathology systems analyse whole-slide images to detect tumours and grade malignancies with reported high sensitivity in selected study settings.
“Digital pathology systems analyse whole-slide images to detect tumours and grade malignancies with reported high sensitivity in selected study settings [33,16].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [16]
The Discussion describes predicting sepsis in ICU patients from electronic medical records, heart rate, and blood pressure; it does not address digital pathology. Searches for whole-slide images, tumour detection, and malignancy grading found no passage supporting the cited claim. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Although various research projects are focusing on CDSS in the ICU, translation into clinical routine is lagging far behind.
“Although various research projects are focusing on CDSS in the ICU, translation into the clinical routine is lagging far behind [15,46-49].”
From Clinical requirements of future patient monitoring in the intensive care unit: qualitative study (2019) · cites this paper as [48]

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Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis
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Authors: Nemati, S., Holder, A., Razmi, F., Stanley, M. D., Clifford, G. D., & Buchman, T. G.
Affiliations: Emory University (US)
Georgia Institute of Technology (US)
Emory Healthcare (US)
ORCID: Nemati, Shamim
Holder, Andre
Razmi, Fereshteh
Clifford, Gari D.
Buchman, Timothy G.
Venue: Critical Care Medicine
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2018-04
Volume: 46
Issue: 4
Pages: 547-553
ISSN: 0090-3493 (ISSN-L)
DOI: 10.1097/ccm.0000000000002936
PMC: PMC5851825
OpenAlex: W2776803885
Semantic Scholar: 26feca29b05ac2abb7841f3a4172d0c28583ca33
Cited by: 832
References: 24
Citations by year: 2026: 112 · 2025: 99 · 2024: 88 · 2023: 107 · 2022: 112 · 2021: 111 · 2020: 93 · 2019: 96 · 2018: 14
FWCI: 43.71
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Critical Care Medicine (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of General Medical Sciences (T32GM095442)
National Institute of Environmental Health Sciences (K01ES025445)
Topics: Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
Keywords: sepsis prediction, early sepsis detection, model interpretability, intensive care unit patients, electronic medical record data, MIMIC-III, Sepsis-3, timing of antibiotic administration
MeSH terms: Academic Medical Centers, Machine Learning, Age Factors, Aged, Blood Pressure, Electrocardiography, Female, Heart Rate, Humans, Intensive Care Units, Male, Middle Aged, ROC Curve, Severity of Illness Index, Sex Factors, Socioeconomic Factors, Time Factors, Comorbidity, Critical Illness, Hospital Mortality, Sepsis, Decision Support Systems, Clinical, Vital Signs, Electronic Health Records, Time-to-Treatment, Organ Dysfunction Scores

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An interpretable machine learning model for accurate prediction of sepsis in the ICU

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