METHODS: Articles in this review referenced EA, were peer-reviewed or gray literature reports published in 2010 to 2016 in English, and were identified using PubMed, Scopus, Web of Science, and Google Scholar.
RESULTS: Fourteen articles described EA use in LMICs. India, Sierra Leone, South Africa, Mozambique, and Rwanda reported building the system to meet country needs and implement a cohesive HIS framework. Jordan and Taiwan focused on specific HIS aspects, ie, disease surveillance and electronic medical records. Five studies informed the context. The Millennium Villages Project employed a "uniform but contextualized" approach to guide systems in 10 countries; Malaysia, Indonesia, and Tanzania used interviews and mapping of existing components to improve HIS, and Namibia used of Activity Theory to identify technology-associated activities to better understand EA frameworks. South Africa, Burundi, Kenya, and Democratic Republic of Congo used EA to move from paper-based to electronic systems.
CONCLUSIONS: Four themes emerged: the importance of multiple sectors and data sources, the need for interoperability, the ability to incorporate system flexibility, and the desirability of open group models, data standards, and software. Themes mapped to EA frameworks and operational components and to health system building blocks and goals. Most articles focused on processes rather than outcomes, as countries are engaged in implementation.
METHODS: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.
RESULTS: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.
CONCLUSIONS: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.
OBJECTIVE: To determine a standardised algorithm to reassess and personalise the treatment COPD patients based on the available evidence.
METHODS: A consensus statement was agreed upon by a panel of pulmonologists in from 11 institutes in Malaysia whose members formed this consensus group.
RESULTS: According to the consensus, which was unanimously adopted, all COPD patients who are currently receiving an ICS-based treatment should be reassessed based on the presence of co-existence of asthma or high eosinophil counts and frequency of moderate or severe exacerbations in the previous 12 months. When that the patients meet any of the aforementioned criteria, then the patient can continue taking ICS-based therapy. However, if the patients do not meet the criteria, then the treatment of patients need to be personalised based on whether the patient is currently receiving long-acting beta-agonists (LABA)/ICS or triple therapy.
CONCLUSION: A flowchart of the consensus providing a guidance to Malaysian clinicians was elucidated based on evidences and international guidelines that identifies the right patients who should receive inhaled corticosteroids and enable to switch non ICS based therapies in patients less likely to benefit from such treatments.
METHODS: This is a meta-analysis of observational studies reporting effect estimates on how HIV is associated with extrapulmonary tuberculosis. We searched for the eligible studies in the electronic databases using search terms related to HIV and extrapulmonary tuberculosis. Where possible, we estimated the summary odds ratios using random effects meta-analysis. We stratified analysis by the type of study design. We assessed heterogeneity of effect estimates within each group of studies was assessed using I (2) test.
RESULTS: Nineteen studies (7 case control studies and 12 cohort studies) were identified for the present study. The pooled analysis shows a significant association between HIV and extrapulmonary tuberculosis (summary odds ratio: 1.3; 95 % confidence interval (CI) 1.05-1.6; I (2): 0 %). In a subgroup analysis with two studies, a significant association was found between CD4+ count less than 100 and the incidence of extrapulmonary tuberculosis (summary OR: 1.31; 95 % CI 1.02-1.68; I (2): 0 %).
CONCLUSIONS: Findings show evidence on the association between extrapulmonary tuberculosis and HIV, based on case control studies. Further studies to understand the mechanisms of interaction of the two pathogens are recommended.