Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient-reported outcomes (PROs) by eight supervised classifiers including a linear model, following hip and knee replacement surgery.

Authors

  • Manuel Huber
    German Research Center for Environmental Health, Institute for Health Economics and Health Care Management, Helmholtz Zentrum München, Postfach 1129, 85758, Neuherberg, Germany. manuel.huber@helmholtz-muenchen.de.
  • Christoph Kurz
    German Research Center for Environmental Health, Institute for Health Economics and Health Care Management, Helmholtz Zentrum München, Postfach 1129, 85758, Neuherberg, Germany.
  • Reiner Leidl
    German Research Center for Environmental Health, Institute for Health Economics and Health Care Management, Helmholtz Zentrum München, Postfach 1129, 85758, Neuherberg, Germany.