Machine-learning models for predicting surgical site infections using patient pre-operative risk and surgical procedure factors.

Journal: American journal of infection control
Published Date: Aug 22, 2022

Abstract

BACKGROUND: Surgical site infections (SSIs) are a significant health care problem as they can cause increased medical costs and increased morbidity and mortality. Assessing a patient's preoperative risk factors can improve risk stratification and help guide the surgical decision-making process. Previous efforts to use preoperative risk factors to predict the occurrence of SSIs have relied upon traditional statistical modeling approaches. The aim of this paper is to develop and validate, using state-of-the-art machine learning (ML) approaches, classification models for the occurrence of SSI to improve upon previous models.

Authors

  • Rabia Emhamed Al Mamlook
    Department of Industrial and Entrepreneurial Engineering & Engineering Management, Western Michigan University, Kalamazoo, MI; Department of Industrial, Engineering University of Zawiya, Al Zawiya City, Libya. Electronic address: Rabia.emhamedm.almamlook@wmich.edu.
  • Lee J Wells
    Department of Industrial and Entrepreneurial Engineering & Engineering Management, Western Michigan University, Kalamazoo, MI.
  • Robert Sawyer
    Department of Surgery, Western Michigan University Homer Stryker School of Medicine, Kalamazoo, MI.

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