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Identifying Depression in the National Health and Nutrition Examination Survey Data using a Deep Learning Algorithm

Oh, Jihoon and Yun, Kyongsik and Maoz, Uri and Kim, Tae-Suk and Chae, Jeong-Ho (2019) Identifying Depression in the National Health and Nutrition Examination Survey Data using a Deep Learning Algorithm. Journal of Affective Disorders, 257 . pp. 623-631. ISSN 0165-0327. http://resolver.caltech.edu/CaltechAUTHORS:20190708-155942887

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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. 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 4,949 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.jad.2019.06.034DOIArticle
Additional Information:© 2019 Published by Elsevier B.V. Received 19 February 2019, Revised 30 April 2019, Accepted 29 June 2019, Available online 4 July 2019.
Funders:
Funding AgencyGrant Number
Korea Health Industry Development InstituteUNSPECIFIED
Ministry of Health (China)UNSPECIFIED
Subject Keywords:Machine learning; Depression; National Health and Nutrition Examination Survey; Deep learning
Record Number:CaltechAUTHORS:20190708-155942887
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190708-155942887
Official Citation:Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae, Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm, Journal of Affective Disorders, Volume 257, 2019, Pages 623-631, ISSN 0165-0327, https://doi.org/10.1016/j.jad.2019.06.034. (http://www.sciencedirect.com/science/article/pii/S0165032719304410)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96929
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Jul 2019 23:40
Last Modified:26 Jul 2019 18:12

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