Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.

Journal: The Journal of arthroplasty
Published Date:

Abstract

Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients' data and for classification and predictive purposes. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field.

Authors

  • Bardia Khosravi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Alexander D Weston
    Radiology Informatics Laboratory, Department of Radiology, Mayo Clinic, Rochester, Minnesota.
  • Fred Nugen
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • John P Mickley
    Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, Minnesota.
  • Hilal Maradit Kremers
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN; Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
  • Cody C Wyles
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Michael J Taunton
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN; Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.