Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Journal: Journal of healthcare engineering

Published Date: Nov 3, 2019

Abstract

OBJECTIVE: Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs.

Authors

Eli Bloch

Department of Industrial Engineering and Management, Afeka Academic College of Engineering, Tel Aviv, Israel.
Tammy Rotem

Department of Industrial Engineering and Management, Afeka Academic College of Engineering, Tel Aviv, Israel.
Jonathan Cohen

Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA.
Pierre Singer

Department of General Intensive Care and Institute for Nutrition Research, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Yehudit Aperstein

Department of Software Engineering, Afeka Academic College of Engineering, Tel Aviv, Israel.

Keywords

Adolescent Adult Aged Aged, 80 and over Algorithms Analysis of Variance Area Under Curve Case-Control Studies Diagnosis, Computer-Assisted Early Diagnosis Female Humans Intensive Care Units Machine Learning Male Middle Aged Models, Biological Sepsis Software Support Vector Machine Vital Signs Young Adult

External Resources

View on PubMed Access via DOI PubMed (31885832)

Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Abstract

Authors

Keywords

External Resources

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