Supervised machine learning and associated algorithms: applications in orthopedic surgery

James A. Pruneski, Ayoosh Pareek, Kyle N. Kunze, R. Kyle Martin, Jón Karlsson, Jacob F. Oeding, Ata M. Kiapour, Benedict U. Nwachukwu, Riley J. Williams

Research output: Contribution to journalReview articlepeer-review

27 Scopus citations

Abstract

Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on “big data” develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

Original languageEnglish (US)
Pages (from-to)1196-1202
Number of pages7
JournalKnee Surgery, Sports Traumatology, Arthroscopy
Volume31
Issue number4
DOIs
StatePublished - Apr 2023

Bibliographical note

Publisher Copyright:
© 2022, The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).

Keywords

  • Machine learning
  • Orthopedics
  • Predictive models
  • Sports Medicine
  • Statistical analysis

PubMed: MeSH publication types

  • Journal Article
  • Review

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