Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review.

Journal: Journal of biomedical informatics
PMID: 39537000

Abstract

BACKGROUND: Clinical pathways are sequences of events followed during the clinical care of a group of patients who meet pre-defined criteria. They have many applications ranging from healthcare evaluation and optimization to clinical decision support. These pathways can be discovered from existing healthcare data, in particular with machine learning which is a family of methods used to learn patterns from data. This review provides a comprehensive overview of the literature concerning the use of machine learning methods for clinical pathway discovery from patient data.

Authors

  • Lillian Muyama
    Inria Paris, Paris, 75013, France; Centre de Recherche des Cordeliers, Inserm, Université Paris Cité, Sorbonne Université, Paris, 75006, France. Electronic address: lillian.muyama@inria.fr.
  • Antoine Neuraz
    Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche des Cordeliers, UMR 1138 Equipe 22, Paris Descartes, Sorbonne Paris Cité University, Paris, France.
  • Adrien Coulet
    LORIA (CNRS, Inria Nancy-Grand Est, University of Lorraine), Campus Scientifique, Nancy, France. adrien.coulet@loria.fr.

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