Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns.

Journal: Journal of biomedical informatics

PMID: 28011233

Abstract

BACKGROUND AND OBJECTIVE: Critical care patient events like sepsis or septic shock in intensive care units (ICUs) are dangerous complications which can cause multiple organ failures and eventual death. Preventive prediction of such events will allow clinicians to stage effective interventions for averting these critical complications.

Authors

Shameek Ghosh

Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia. Electronic address: Shameek.Ghosh@student.uts.edu.au.
Jinyan Li
Longbing Cao

Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia. Electronic address: Longbing.Cao@uts.edu.au.
Kotagiri Ramamohanarao

Department of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia. Electronic address: kotagiri@unimelb.edu.au.

Keywords

Blood Pressure Critical Care Forecasting Heart Rate Humans Intensive Care Units Machine Learning Multiple Organ Failure Risk Assessment Shock, Septic

External Resources

View on PubMed Access via DOI PubMed (28011233)

Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns.

Abstract

Authors

Keywords

External Resources

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