Jihoon Oh1, Kyongsik Yun2, Uri Maoz3, Tae-Suk Kim1, Jeong-Ho Chae4. 1. 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. 2. 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. 3. 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. 4. 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.
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 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.
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.
Authors: Rafael Salas-Zárate; Giner Alor-Hernández; María Del Pilar Salas-Zárate; Mario Andrés Paredes-Valverde; Maritza Bustos-López; José Luis Sánchez-Cervantes Journal: Healthcare (Basel) Date: 2022-02-01