An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Journal: Critical care medicine

Published Date: Apr 1, 2018

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.

Authors

Shamim Nemati

Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.
Andre Holder

Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA.
Fereshteh Razmi

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.
Matthew D Stanley

Department of Surgery, Emory University School of Medicine, Atlanta, GA.
Gari D Clifford

Department of Biomedical Informatics, Emory University, Atlanta, GA, United States.
Timothy G Buchman

Department of Surgery, Emory University School of Medicine, Atlanta, GA.

Keywords

Academic Medical Centers Age Factors Aged Blood Pressure Comorbidity Critical Illness Decision Support Systems, Clinical Electrocardiography Electronic Health Records Female Heart Rate Hospital Mortality Humans Intensive Care Units Machine Learning Male Middle Aged Organ Dysfunction Scores ROC Curve Sepsis Severity of Illness Index Sex Factors Socioeconomic Factors Time Factors Time-to-Treatment Vital Signs

External Resources

View on PubMed Access via DOI PubMed (29286945)

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Abstract

Authors

Keywords

External Resources

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