-A machine learning model to predict surgical site infection after surgery of lower extremity fractures.

Journal: International orthopaedics

PMID: 38700699

Abstract

PURPOSE: This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical site infection in patients with lower extremity fractures.

Authors

Jose M Gutierrez-Naranjo

Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA. jmgutierrezn@gmail.com.
Alvaro Moreira

Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA.
Eduardo Valero-Moreno

Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA.
Travis S Bullock

Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA.
Liliana A Ogden

Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA.
Boris A Zelle

Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA. zelle@uthscsa.edu.

Keywords

Adult Aged Algorithms Female Fractures, Bone Humans Lower Extremity Machine Learning Male Middle Aged Retrospective Studies Risk Assessment Risk Factors Surgical Wound Infection Young Adult

External Resources

View on PubMed Access via DOI PubMed (38700699)

-A machine learning model to predict surgical site infection after surgery of lower extremity fractures.

Abstract

Authors

Keywords

External Resources

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