An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU
Emory University · Pulmonary and Allergy Associates · +2 more institutions
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
6Topics & keywords
Topics
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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