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Research on the application of artificial intelligence in the field of depression

Published: 22 December 2021 Publication History

Abstract

Depression is one of the most common mental disorders, with a high prevalence, disability and death rate, which seriously affect population health and social functions. It is the main type of mood disorder with significant and lasting depression as the main clinical feature. In order to seek more objective, more efficient, and more advanced methods of diagnosis and treatment, artificial intelligence technology in recent years seems to provide brand new possibilities in the prevention, diagnosis, intervention, treatment, and rehabilitation of depression, and some preliminary results have been achieved. In this regard, this article will review the current application and research of artificial intelligence in the field of depression, and discuss the potential problems and challenges that follow.

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  1. Research on the application of artificial intelligence in the field of depression

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    ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences
    October 2021
    593 pages
    ISBN:9781450395588
    DOI:10.1145/3500931
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 December 2021

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    Author Tags

    1. artificial intelligence
    2. deep learning
    3. depression
    4. machine learning

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    Overall Acceptance Rate 53 of 112 submissions, 47%

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