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Machine Learning in the Analysis of Mental Disease

Authors:

Lori Kim,

Meng Han,

Rongkai GuoAuthors Info & Claims

ACMSE '20: Proceedings of the 2020 ACM Southeast Conference

Pages 316 - 317

https://doi.org/10.1145/3374135.3385299

Published: 25 May 2020 Publication History

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Abstract

Mental Health issues affect millions of people every year and deter the ability of an individual to live a fulfilling life. 1 in 5 U.S. adults experience some type of mental illness every year. Even though in the U.S. there are progressive policies and initiatives to help those with mental illnesses, there are still many people suffering and not receiving the help they need. There are different factors and reasons behind why some individuals are hesitant to receive care. Location, stigma, and the fear of treatment are some of the reasons, but with the help of technology like machine learning there can be a access to help everywhere. This work provides general overview on the mental disease particularly to review the machine learning application in the mental disease analysis to provide the best overview of this emerging direction.

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Cited By

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Kadkhoda EKhorasani MPourgholamali FKahani MArdani A(2022)Bipolar disorder detection over social mediaInformatics in Medicine Unlocked10.1016/j.imu.2022.10104232(101042)Online publication date: 2022
https://doi.org/10.1016/j.imu.2022.101042
He JHan MYu LMei C(2020)Lung Pattern Classification Via DCNN2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378090(3737-3743)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378090

Index Terms

Machine Learning in the Analysis of Mental Disease
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

ACMSE '20: Proceedings of the 2020 ACM Southeast Conference

April 2020

337 pages

ISBN:9781450371056

DOI:10.1145/3374135

Conference Chair:
Morris Chang,
Program Chair:
Dan Lo,
Publications Chair:
Eric Gamess

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 May 2020

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ACM SE '20

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ACM SE '20: 2020 ACM Southeast Conference

April 2 - 4, 2020

FL, Tampa, USA

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Kadkhoda EKhorasani MPourgholamali FKahani MArdani A(2022)Bipolar disorder detection over social mediaInformatics in Medicine Unlocked10.1016/j.imu.2022.10104232(101042)Online publication date: 2022
https://doi.org/10.1016/j.imu.2022.101042
He JHan MYu LMei C(2020)Lung Pattern Classification Via DCNN2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378090(3737-3743)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378090

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Recommendations

Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

Prediction and Analysis of Multiple Causes of Mental Health Problems Based on Machine Learning

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