Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.

Journal: Applied clinical informatics
Published Date: Sep 1, 2021

Abstract

OBJECTIVE: The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts.

Authors

  • Lin Lawrence Guo
    Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
  • Stephen R Pfohl
    Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA 94305, United States of America. Electronic address: spfohl@stanford.edu.
  • Jason Fries
    1Stanford University, Stanford, CA USA.
  • Jose Posada
    Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
  • Scott Lanyon Fleming
    Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States.
  • Catherine Aftandilian
    Division of Pediatric Hematology/Oncology, Stanford University, Palo Alto, United States.
  • Nigam Shah
    Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, United States.
  • Lillian Sung
    Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G1X8, Canada. lillian.sung@sickkids.ca.

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