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Advances in Psychological Science    2020, Vol. 28 Issue (1) : 111-127     DOI: 10.3724/SP.J.1042.2020.00111
Regular Articles |
Computational psychiatry: A new perspective on research and clinical applications in depression
OU Jianxin1,2,WU Yin1,2,LIU Jinting1,2,LI Hong1,2,3()
1 School of Psychology, Shenzhen University
2 Shenzhen Key Laboratory of Affective and Social Cognitive Science
3 Shenzhen Institute of Neuroscience, Shenzhen 518060, China
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Abstract  

Depression, a complex and heterogeneous mental disorder, leads to great global burdens of disease. Although diagnosis based on nosology is broadly used in several domains, it is still unable to direct the exploration of pathological mechanism of depression. In addition, several treatments developed by this diagnosis have poor outcomes due to its low prediction validity. Computational approaches to psychiatry remedy those limitations and help to improve understanding, prediction and treatment for depression by two complementary approaches: data-driven and theory-driven. Theory-driven approaches apply models to multiple levels of analysis from the prior knowledge or hypojournal of depression. Data-driven approaches, however, adopt machine-learning methods to analyze high-dimensional data to improve the diagnostic and predictive accuracies of depression, and eventually, promote the treatment effects. With the development and combination of these two approaches as well as the integration of resources, it is promising to cure depression and prevent it from occurrence.

Keywords depression      computational psychiatry      computational models      machine learning      diagnosis      treatment     
ZTFLH:  R395  
Corresponding Authors: Hong LI     E-mail: lihongszu@szu.edu.cn
Issue Date: 21 November 2019
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Jianxin OU
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Cite this article:   
Jianxin OU,Yin WU,Jinting LIU, et al. Computational psychiatry: A new perspective on research and clinical applications in depression[J]. Advances in Psychological Science, 2020, 28(1): 111-127.
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http://journal.psych.ac.cn/xlkxjz/EN/10.3724/SP.J.1042.2020.00111     OR     http://journal.psych.ac.cn/xlkxjz/EN/Y2020/V28/I1/111
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