Systematic review of Internet of medical things for cardiovascular disease prevention among Australian first nations.

Chronic diseases within Indigenous communities constitute the most compelling ill-health burdens and treatment inequalities, particularly in rural and remote Australia. In response to these vital issues, a systematic literature review of the adoption of wearable, Artificial Intelligence-driven, electrocardiogram sensors, in a telehealth Internet of Medical Things (IoMT) context was conducted to scale up rural Indigenous health. To this end, four preselected scientific databases were chosen for data extraction to align with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique.

Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.

Many individuals visit rural telemedicine centres to obtain safe and effective health remedies for their physical and emotional illnesses. This study investigates the antecedents of patients' satisfaction relating to telemedicine adoption in rural public hospitals settings in Bangladesh through the adaptation of Expectation Disconfirmation Theory extended by Social Cognitive Theory. This research advances a theoretically sustained prediction model forecasting patients' satisfaction with telemedicine to enable informed decision making.

An approach to forecast impact of Covid-19 using supervised machine learning model.

The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data.

Expectations of telemedicine health service adoption in rural Bangladesh.

This study analyzes the antecedent factors that influence patients' expectations of telemedicine adoption in centres hosted by rural public hospitals in Bangladesh. It examines five antecedents of patients' expectations of telemedicine adoption-self-efficacy, telemedicine experience, knowledge, enjoyment, and prior satisfaction. A conceptual research model was formulated, encompassing a set of hypotheses that were developed and tested by employing partial least squares structural equation modelling.