PMID- 31357159
OWN - NLM
STAT- MEDLINE
DCOM- 20200629
LR  - 20200629
IS  - 1573-2517 (Electronic)
IS  - 0165-0327 (Linking)
VI  - 257
DP  - 2019 Oct 1
TI  - Identifying depression in the National Health and Nutrition Examination Survey 
      data using a deep learning algorithm.
PG  - 623-631
LID - S0165-0327(19)30441-0 [pii]
LID - 10.1016/j.jad.2019.06.034 [doi]
AB  - 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. METHODS: Customized deep-neural-network and 
      machine-learning classifiers were assessed using survey data from 19,725 
      participants from the NHANES database (from 1999 through 2014) and 4949 from the 
      South Korea NHANES (K-NHANES) database in 2014. RESULTS: A deep-learning 
      algorithm showed area under the receiver operating characteristic curve (AUCs) of 
      0.91 and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The 
      deep-learning algorithm trained with serial datasets (NHANES, from 1999 to 2012), 
      predicted the prevalence of depression in the following two years of data 
      (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained 
      with NHANES could further predict depression in K-NHANES. There, logistic 
      regression had the highest performance (AUC, 0.77) followed by deep learning 
      algorithm (AUC, 0.74). CONCLUSIONS: Deep neural-networks managed to identify 
      depression well from other health and demographic factors in both the NHANES and 
      K-NHANES datasets. The deep-learning algorithm was also able to predict 
      depression relatively well on new data set-cross temporally and cross nationally. 
      Further research can delineate the clinical implications of machine learning and 
      deep learning in detecting disease prevalence and progress as well as other risk 
      factors for depression and other mental illnesses.
CI  - Copyright (c) 2019. Published by Elsevier B.V.
FAU - Oh, Jihoon
AU  - Oh J
AD  - 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.
FAU - Yun, Kyongsik
AU  - Yun K
AD  - 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.
FAU - Maoz, Uri
AU  - Maoz U
AD  - 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.
FAU - Kim, Tae-Suk
AU  - Kim TS
AD  - 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.
FAU - Chae, Jeong-Ho
AU  - Chae JH
AD  - 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.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190704
PL  - Netherlands
TA  - J Affect Disord
JT  - Journal of affective disorders
JID - 7906073
SB  - IM
MH  - Adult
MH  - *Algorithms
MH  - Databases, Factual
MH  - *Deep Learning
MH  - Depression/*epidemiology
MH  - Female
MH  - Humans
MH  - Machine Learning
MH  - Male
MH  - Middle Aged
MH  - Neural Networks, Computer
MH  - Nutrition Surveys
MH  - ROC Curve
MH  - Republic of Korea
MH  - Risk Factors
OTO - NOTNLM
OT  - Deep learning
OT  - Depression
OT  - Machine learning
OT  - National Health and Nutrition Examination Survey
EDAT- 2019/07/30 06:00
MHDA- 2020/07/01 06:00
CRDT- 2019/07/30 06:00
PHST- 2019/02/19 00:00 [received]
PHST- 2019/04/30 00:00 [revised]
PHST- 2019/06/29 00:00 [accepted]
PHST- 2019/07/30 06:00 [pubmed]
PHST- 2020/07/01 06:00 [medline]
PHST- 2019/07/30 06:00 [entrez]
AID - S0165-0327(19)30441-0 [pii]
AID - 10.1016/j.jad.2019.06.034 [doi]
PST - ppublish
SO  - J Affect Disord. 2019 Oct 1;257:623-631. doi: 10.1016/j.jad.2019.06.034. Epub 
      2019 Jul 4.