Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions.

Journal: World journal of surgery

PMID: 34704147

Abstract

INTRODUCTION: Patients undergoing laparotomy for emergency general surgery (EGS) conditions, constitute a high-risk group with poor outcomes. These patients have a high prevalence of comorbidities. This study aims to identify patient factors, physiological and time-related factors, which place patients into a group at increased risk of mortality.

Authors

Michelle T D Smith

Department of Anesthetics and Critical Care, Greys Hospital, University of KwaZulu-Natal, 201 Townbush road, Pietermaritzburg, 3200, South Africa. mtdsmith1708@yahoo.com.
John L Bruce

Department of Surgery, University of KwaZulu-Natal, Durban, South Africa.
Damian L Clarke

Department of Surgery, University of KwaZulu-Natal, Durban, South Africa.

Keywords

Comorbidity Emergencies Hospital Mortality Humans Laparotomy Machine Learning Middle Aged Postoperative Complications Retrospective Studies Risk Factors

External Resources

View on PubMed Access via DOI PubMed (34704147)

Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions.

Abstract

Authors

Keywords

External Resources

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