An adaptive data-driven architecture for mental health care applications.

Journal: PeerJ
Published Date:

Abstract

BACKGROUND: In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders.

Authors

  • Aishwarya Sundaram
    Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Hema Subramaniam
    Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Siti Hafizah Ab Hamid
    Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Azmawaty Mohamad Nor
    Department of Educational Psychology and Counselling, Faculty of Education, Universiti Malaya, Kuala Lumpur, Malaysia.