An adaptive data-driven architecture for mental health care applications

Area of Science:

• Computer Science
• Health Informatics
• Machine Learning

Background:

• Digital data generation is increasing, necessitating robust data-driven architectures for positive computing.
• The COVID-19 pandemic highlighted the need for flexible mental health care systems.
• Machine learning (ML) models can identify individuals at high risk for mental disorders, improving care accessibility.

Approach:

• A systematic literature review following PRISMA guidelines identified key architectural paradigms.
• An adaptive, data-driven architecture was designed using six fundamental paradigms.
• Expert validation using a Likert scale confirmed the architecture's effectiveness, achieving a mean score above four.

Key Points:

• Six core paradigms were identified for designing effective software architecture.
• The developed adaptive data-driven architecture was validated by professional experts.
• A prototype architecture for predicting pandemic anxiety was created to demonstrate practical application.

Conclusions:

• The research successfully created and validated an adaptive, data-driven architecture for mental health care.
• This architecture leverages ensemble machine learning for positive computing and personalized interventions.
• The findings suggest improved mental health care outcomes and accessibility through data-driven approaches.