Abstract

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Title:: Advances in the application of machine learning in auxiliary diagnosis of depression

Author(s):: DIAO Yunheng1; 2; WANG Huiying1; 2; DONG Jiao1; ZHU Yihan3; SHAO Qiujing1; FENG Laipeng1; 2; WANG Changhong1; 2; 4; 1. The Second Affiliated Hospital of Xinxiang Medical University (Henan Mental Hospital), Xinxiang 453002, China 2. Henan Key Laboratory of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China 3. The Second Clinical College, Xinxiang Medical University, Xinxiang 453002, China 4. Henan Provincial Psychological Assistance Cloud Platform and Application Engineering Research Center, Xinxiang 453002, China

Abstract:: Abstract: With the development of technologies in the diagnosis and treatment of depression, the volume of clinical data related to depression is rapidly expanding, and machine learning technology is just suitable for large quantity, multi-dimensional, multi-mode data. The characteristics of the data obtained in the diagnosis and treatment of depression can be automatically learned by machine learning technology, and the data features can be used for the diagnosis of depression and the prediction of curative effect, so as to achieve the purpose of auxiliary diagnosis of depression. Herein a systematic analysis is carried out on the literatures about the application of machine learning on various categories of clinical data, and the research process and methods of machine learning in the diagnosis of depression are summarized, and finally, the research directions and challenges in the future are presented.