articleCritical Care MedicineDec 29, 2017Closed access

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

Emory University · Pulmonary and Allergy Associates · +2 more institutions

PubMed
Indexed incrossrefpubmed

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.

Citation impact

810
total citations
FWCI
38.05
Percentile
100%
References
24
Citations per year

Authors

6

Topics & keywords

Keywords
  • Medicine
  • Sepsis
  • Cohort
  • Intensive care
  • Emergency medicine
  • Intensive care medicine
  • Medical record
  • Internal medicine
UN Sustainable Development Goals
  • Good health and well-being
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