Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm.

Journal: Journal of affective disorders
Published Date: Jul 4, 2019

Abstract

BACKGROUND: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Authors

  • Jihoon Oh
    Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
  • Kyongsik Yun
    Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA; Bio-Inspired Technologies and Systems, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA.
  • Uri Maoz
    Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA; Computational Neuroscience, Health and Behavioral Sciences and Brain Institute, Chapman University, Orange, CA 92866, USA; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA 92866, USA; Department of Anesthesiology, School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
  • Tae-Suk Kim
    Department of Psychiatry, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea.
  • Jeong-Ho Chae
    Department of Psychiatry, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, 222 Banpo-Daero, Seocho-Gu, Seoul 06591, Republic of Korea. Electronic address: alberto@catholic.ac.kr.

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