Artificial Intelligence based Early Detection and Timely Diagnosis of Mental Illness - A Review

Singh, Palak and Srinivas, Kandala Kalyana and Peddi, Anudeep and Shabarinath, Bb and Neelima, I and Bhagavathi, Kandala Aditya (2022) Artificial Intelligence based Early Detection and Timely Diagnosis of Mental Illness - A Review. In: 2022 International Mobile and Embedded Technology Conference, MECON 2022, 10 March 2022 through 11 March 2022, Noida.

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Mental health includes the overall well-being of an individual. It affects the daily life style and relationships of an individual. Physical health has always found space in regular discussions, whereas mental health has always been side-lined. Mental health and physical health are equally important and interrelated components of overall health. It is essential to take care of mental health in the same way as physical health. The health industry has successfully found new ways to treat physical diagnoses. There are solutions to all sorts of physical problems. But, the diagnosis of mental health is still a point of research. The turn of the century has witnessed machine learning, data mining, and several other technologies in diagnosing physical health problems like cancer, Arrhythmia. Early identification of mental illness is major setback in providing timely diagnosis. In this paper we aim to explore the pre-existing work of several technologies in the field of psychiatric care. The goal behind writing this paper is to speed up the existing research on early identification of mental disorders and faster and timely treatment of the same. © 2022 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Artificial Intelligence; Automated system; Bipolar Disorder; Depression Disorder; Depression via speech; Diagnosis; Mental Health
Subjects: Others > Medicine
Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 20 Jul 2022 10:17
Last Modified: 20 Jul 2022 10:17
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