Machine Learning Improves Prediction Over Logistic Regression on Resected Colon Cancer Patients.

Journal: The Journal of surgical research
Published Date: Mar 11, 2022

Abstract

INTRODUCTION: Despite advances, readmission and mortality rates for surgical patients with colon cancer remain high. Prediction models using regression techniques allows for risk stratification to aid periprocedural care. Technological advances have enabled large data to be analyzed using machine learning (ML) algorithms. A national database of colon cancer patients was selected to determine whether ML methods better predict outcomes following surgery compared to conventional methods.

Authors

  • Grey Leonard
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: grey.leonard@UTSouthwestern.edu.
  • Charles South
    Department of Statistical Science, Southern Methodist University, Dallas, Texas.
  • Courtney Balentine
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas; VA North Texas Healthcare System, Dallas, Texas; UTSW Surgical Center for Outcomes, Implementation and Novel Interventions (S-COIN), Dallas, Texas.
  • Matthew Porembka
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • John Mansour
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Sam Wang
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Adam Yopp
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Patricio Polanco
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Herbert Zeh
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Mathew Augustine
    Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas; VA North Texas Healthcare System, Dallas, Texas.

Keywords

External Resources

Popular Topics
Recent Journals