Sundaram, Aishwarya and Subramaniam, Hema and Ab Hamid, Siti Hafizah and Nor, Azmawaty Mohamad (2024) An adaptive data-driven architecture for mental health care applications. PeerJ, 12. e17133. ISSN 2167-8359, DOI https://doi.org/10.7717/peerj.17133.
Full text not available from this repository.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. Objective: Therefore, this research aims to create a flexible mental health care architecture that leverages data -driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data -driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method: Following the Preferred Reporting Items for Systematic Reviews and Meta -Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results: Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data -driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed. Subjects Psychiatry and Psychology, Computational Science, Mental Health, Healthcare Services
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education Malaysia for the Fundamental Research Grant Scheme (FRGS/1/2022/SS09/UM/02/4) |
| Uncontrolled Keywords: | Mental health; Big data; Data-driven architectures; Machine learning algorithms; Anxiety prediction; Health care |
| Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Education Institute of Advanced Studies |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Oct 2024 03:17 |
| Last Modified: | 21 Oct 2024 03:17 |
| URI: | http://eprints.um.edu.my/id/eprint/45400 |
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