Computational Methods to Predict Suicide Ideation among Adolescents

Title

Computational Methods to Predict Suicide Ideation among Adolescents

Creator

Rappai S.; Ramasamy G.

Description

Suicide has been a prominent cause of death worldwide, regardless of age, sex, geography, and so on, and predominantly suicide among teens, increased as the years have passed. Suicide ideation, suicide risk, suicide attempts have been studied extensively, and the most common cause has been identified as depression, followed by familial concerns, hereditary factors, stress, avoidance fear, and a variety of other variables. When visited by a doctor, most adolescents are unaware of their mental state and hence do not take action on their own or are not assisted by family or peer members to overcome their fear of social stigma or the treatment they must undergo. According to popular belief, early treatment and detection are the most effective ways to reduce the risk of suicide. As a result, the focus of this study is to illustrate some of the computational strategies utilized in deep learning and machine learning fields to detect kids at risk of suicide 2022 IEEE.

Source

Proceedings - IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

deep learning; machine learning; mental health; neural network analysis; suicide ideation; suicide prevention; suicide risk

Coverage

Rappai S., Christ(Deemed to Be University), Department of Computer Science, Bengaluru, India; Ramasamy G., Christ(Deemed to Be University), Department of Computer Science, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 978-166549529-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ACCAI53970.2022.9752645

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128840328&doi=10.1109%2fACCAI53970.2022.9752645&partnerID=40&md5=f839dc611c93bb412ae8bdd1b2827541