Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 1, 2020

Abstract

OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological meaning, and visualization. We hypothesized that clustering could discover prognostic groups from patients with chronic lymphocytic leukemia, a disease that provides biological validation through well-understood outcomes.

Authors

  • Caitlin E Coombes
    The Ohio State University College of Medicine, Columbus, Ohio, USA.
  • Zachary B Abrams
    Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.
  • Suli Li
    Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
  • Lynne V Abruzzo
    Department of Pathology, The Ohio State University, Columbus, Ohio, USA.
  • Kevin R Coombes
    Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.

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