Work

New method for assessing suicide ideation based on an attention mechanism and spiking neural network

Year: 2024

Type: article

Abstract: The COVID-19 pandemic has had a substantial effect on global mental health, leading to increased depression and suicide ideation (SI), particularly among young adults. This study introduces a novel method for enhancing SI assessment in young adults with depression, utilizing machine learning (ML) techniques applied to structural magnetic resonance imaging (SMRI) data. SMRI data from 20 individuals with depression and 60 healthy controls were analyzed. A hybrid ML algorithm, integrating self-attention mechanism and evolving spiking neural networks, successfully classified depression with 94% accuracy, 100% sensitivity, 92% specificity, and an area under the curve of 0.96. These results offer potential for enhancing mental health intervention and support in the context of the ongoing and post-pandemic period influenced by COVID-19.

Source: IAES International Journal of Artificial Intelligence

DOI: 10.11591/ijai.v14.i1.pp350-357

Authors: Corrine Francis, Abdulrazak Yahya Saleh Al-Hababi

Institution: Universiti Malaysia Sarawak

Language: English

Topic: Suicide and Self-Harm Studies

Subfield: Clinical Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Universiti Malaysia Sarawak