Data Analytics in Mental Healthcare
Joint Authors
Mirza, Farhaan
Jamil, Noreen
Naeem, M. Asif
Kamran Ul haq, Ayesha
Khattak, Amira
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Worldwide, about 700 million people are estimated to suffer from mental illnesses.
In recent years, due to the extensive growth rate in mental disorders, it is essential to better understand the inadequate outcomes from mental health problems.
Mental health research is challenging given the perceived limitations of ethical principles such as the protection of autonomy, consent, threat, and damage.
In this survey, we aimed to investigate studies where big data approaches were used in mental illness and treatment.
Firstly, different types of mental illness, for instance, bipolar disorder, depression, and personality disorders, are discussed.
The effects of mental health on user’s behavior such as suicide and drug addiction are highlighted.
A description of the methodologies and tools is presented to predict the mental condition of the patient under the supervision of artificial intelligence and machine learning.
American Psychological Association (APA)
Kamran Ul haq, Ayesha& Khattak, Amira& Jamil, Noreen& Naeem, M. Asif& Mirza, Farhaan. 2020. Data Analytics in Mental Healthcare. Scientific Programming،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1208987
Modern Language Association (MLA)
Kamran Ul haq, Ayesha…[et al.]. Data Analytics in Mental Healthcare. Scientific Programming No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1208987
American Medical Association (AMA)
Kamran Ul haq, Ayesha& Khattak, Amira& Jamil, Noreen& Naeem, M. Asif& Mirza, Farhaan. Data Analytics in Mental Healthcare. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1208987
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208987