Identifying bladder rupture following traumatic pelvic fracture: A machine learning approach.

Journal: Injury

PMID: 31866131

Abstract

INTRODUCTION: Bladder rupture following blunt pelvic trauma is rare though can have significant sequelae. We sought to determine whether machine learning could help predict the presence of bladder injury using certain factors at the time of presentation of blunt pelvic trauma.

Authors

Alexandria M Hertz

Madigan Army Medical Center, 9040 Jackson Ave, Tacoma, WA 98431, USA. Electronic address: Alexandria.m.hertz.mil@mail.mil.
Nicholas M Hertz

Madigan Army Medical Center, 9040 Jackson Ave, Tacoma, WA 98431, USA; Vanderbilt University Medical Center, USA.
Niels V Johnsen

Vanderbilt University Medical Center, USA. Electronic address: niels.v.johnsen@vumc.org.

Keywords

Area Under Curve Bayes Theorem Databases, Factual Decision Trees Fractures, Bone Humans Logistic Models Machine Learning Pelvic Bones Radiography Retrospective Studies ROC Curve Rupture Urinary Bladder Urinary Bladder Diseases Washington Wounds, Nonpenetrating

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View on PubMed Access via DOI PubMed (31866131)

Identifying bladder rupture following traumatic pelvic fracture: A machine learning approach.

Abstract

Authors

Keywords

External Resources

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