Deep learning uncertainty quantification for clinical text classification.

Journal: Journal of biomedical informatics
Published Date: Dec 13, 2023

Abstract

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated.

Authors

  • Alina Peluso
    Advanced Computing for Health Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.
  • Ioana Danciu
    Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37830, USA; Department of Biomedical Informatics, Vanderbilt University, 2525 West End Avenue, Nashville, TN 37203, USA.
  • Hong-Jun Yoon
  • Jamaludin Mohd Yusof
    Los Alamos National Laboratory, Los Alamos, NM 87545, United States.
  • Tanmoy Bhattacharya
    Los Alamos National Laboratory, Los Alamos, NM 87545, United States.
  • Adam Spannaus
    Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.
  • Noah Schaefferkoetter
    Oak Ridge National Lab, Oak Ridge, TN, USA.
  • Eric B Durbin
    University of Kentucky, Lexington, KY.
  • Xiao-Cheng Wu
    Department of Epidemiology, Louisiana State University New Orleans School of Public Health, New Orleans, LA 70112, United States.
  • Antoinette Stroup
    New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, 08901, United States of America. Electronic address: nan.stroup@rutgers.edu.
  • Jennifer Doherty
    Utah Cancer Registry, University of Utah School of Medicine, Salt Lake City, UT 84132, United States of America. Electronic address: Jen.Doherty@hci.utah.edu.
  • Stephen Schwartz
    Fred Hutchinson Cancer Research Center, Epidemiology Program, Seattle, WA 98109, USA.
  • Charles Wiggins
    University of New Mexico, Albuquerque, NM 87131, USA.
  • Linda Coyle
    Information Management Services Inc, Calverton, Maryland, USA.
  • Lynne Penberthy
    Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.
  • Georgia D Tourassi
  • Shang Gao
    Department of Orthopedics, Orthopedic Center of Chinese PLA, Southwest Hospital, Third Military Medical University, Chongqing, 400038, P.R.China.

Keywords

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