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.
METHODS: We used the 11-item Duke Social Support Index to assess perceived social support through a face-to-face interview. Higher scores indicate better social support. Linear regression analysis was carried out to determine the factors that influence perceived social support by adapting the conceptual model of social support determinants and its impact on health.
RESULTS: A total of 3959 respondents aged ≥60 years completed the Duke Social Support Index. The estimated mean Duke Social Support Index score was 27.65 (95% CI 27.36-27.95). Adjusted for confounders, the factors found to be significantly associated with social support among older adults were monthly income below RM1000 (-0.8502, 95% CI -1.3523, -0.3481), being single (-0.5360, 95% CI -0.8430, -0.2290), no depression/normal (2.2801, 95% CI 1.6666-2.8937), absence of activities of daily living (0.9854, 95% CI 0.5599-1.4109) and dependency in instrumental activities of daily living (-0.3655, 95% CI -0.9811, -0.3259).
CONCLUSION: This study found that low income, being single, no depression, absence of activities of daily living and dependency in instrumental activities of daily living were important factors related to perceived social support among Malaysian older adults. Geriatr Gerontol Int 2020; 20: 63-67.
METHODS: This retrospective, observational study included children aged ≤12 years old hospitalised with hMPV or RSV, confirmed via direct fluorescent antibody (DFA) methods, between 1 July to 30 October 2022 at Hospital Tuanku Ja'afar Seremban, Malaysia. Demographic, clinical presentation, resource utilisation and outcome data were analysed. Propensity score matching was used to balance cohorts based on key demographic and clinical characteristics.
RESULTS: This study included 192 patients, comprising 112 with hMPV and 80 with RSV. hMPV patients were older (median age 20.5 vs. 9.4 months, p