Research progress of deep learning algorithm in the auxiliary diagnosis of depression based on behavior analysis
10.3760/cma.j.cn113661-20191204-00411
- VernacularTitle:深度学习算法在面向行为分析的抑郁症辅助诊断中的研究进展
- Author:
Simeng MA
1
;
Yong CAO
;
Peilin WANG
;
Jun YANG
;
Zhongchun LIU
Author Information
1. 武汉大学人民医院精神卫生中心 430060
- Publication Type:Journal Article
- Keywords:
Depressive disorder;
Deep learning;
Computer vision;
Behavioral analysis;
Multimodal data fusion
- From:
Chinese Journal of Psychiatry
2020;53(5):460-463
- CountryChina
- Language:Chinese
-
Abstract:
In the past decade, the development of artificial intelligence technology has gradually spread from academia to industry, and its application in the medical field has profoundly deepened. As the driving engine of artificial intelligence, the machine learning and deep learning， based on data and algorithms, contribute the core technology of artificial intelligence (AI), given the limitation of technology and the ethical consideration, AI can be more acceptedits role in auxiliary decision-making instead of independent diagnosis and treatment. Depression is one of the world wide popular mental condition, and its early diagnosis depression promot estimely treatment. AI technology may help to screen depression by analysis of the various characteristics: the physiological indicators, facial expressions, voice intonation, text semantics, gesture behavior, and with all above information calcucated together to establish a model of depression auxiliary diagnosis system. This paper summarizes the literature on the AI auxiliary diagnosis for depression based on different models as well as multimodal data fusion.
