Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review.

Journal: ANZ journal of surgery
Published Date: Apr 10, 2024

Abstract

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.

Authors

  • Khashayar Ghadirinejad
    The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia.
  • Roohollah Milimonfared
    The Medical Device Research Institute, Flinders University, Adelaide, Australia.
  • Mark Taylor
    The Medical Device Research Institute, Flinders University, Adelaide, Australia.
  • Lucian B Solomon
    Department of Orthopaedics and Trauma Service, Royal Adelaide Hospital, Adelaide, Australia; Centre for Orthopaedic and Trauma Research, The University of Adelaide, Adelaide, Australia.
  • Stephen Graves
    Department of Surgery, Epworth HealthCare, The University of Melbourne, Parkville, Victoria, Australia.
  • Nicole Pratt
    The Australian Orthopaedic Association National Joint Replacement Registry, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
  • Richard de Steiger
    Quality Use of Medicines and Pharmacy Research Centre, School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia.
  • Reza Hashemi
    The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia.

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