Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature.

Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.

Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.

Framework of sensor-based monitoring for pervasive patient care.

Sensor-based health systems can often become difficult to use, extend and sustain. The authors propose a framework for designing sensor-based health monitoring systems aiming to provide extensible and usable monitoring services in the scope of pervasive patient care. The authors' approach relies on a distributed system for monitoring the patient health status anytime-anywhere and detecting potential health complications, for which healthcare professionals and patients are notified accordingly.

A Survey of Mobile Phone Sensing, Self-Reporting, and Social Sharing for Pervasive Healthcare.

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e.

Development and usability of a personalized sensor-based system for pervasive healthcare.

Although a plethora of remote health monitoring systems have been proposed for chronic conditions, the challenge posed by the changing patient needs and the requirement for personalization in health monitoring to move beyond proprietary, difficult to extend, and unsustainable solutions still pertains. In this direction, we describe a mobile health system based on a smartphone, portable/wearable sensors for measuring the patient's physiological parameters, and back-end platforms for the health professionals to monitor the patient condition and configure monitoring plans in an individualized manner. A prototype system was developed based on a Service-oriented Architecture and integrating commercially available sensing devices.