Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm.

Journal: American journal of surgery
Published Date: Jul 24, 2018

Abstract

BACKGROUND: Machine-learning can elucidate complex relationships/provide insight to important variables for large datasets. This study aimed to develop an accurate model to predict neonatal surgical site infections (SSI) using different statistical methods.

Authors

  • Marisa A Bartz-Kurycki
    McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX, 77030, USA.
  • Charles Green
    McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX, 77030, USA.
  • Kathryn T Anderson
    McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX, 77030, USA.
  • Adam C Alder
    Children's Medical Center of Dallas, 1935 Medical District Dr, Dallas, TX, 75235, USA.
  • Brian T Bucher
    School of Medicine, University of Utah, Salt Lake City, Utah, US.
  • Robert A Cina
    Medical University of South Carolina, 180 Calhoun St, Charleston, SC, 29401, USA.
  • Ramin Jamshidi
    Phoenix Children's Hospital, 1919 E Thomas Rd, Phoenix, AZ, 85016, USA.
  • Robert T Russell
    Department of Pediatric Surgery, Children's Hospital of Alabama, Birmingham, Alabama.
  • Regan F Williams
    University of Tennessee Health Science Center, 910 Madison Ave, Memphis, TN, 38163, USA.
  • KuoJen Tsao
    McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX, 77030, USA. Electronic address: KuoJen.Tsao@uth.tmc.edu.

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