Smart Learning Using Big and Small Data for Mobile and IOT e-Health

Abstract

In this chapter, we provide a snapshot of the state-of-the-art research in mobile and IOT e-health studies that leverage AI technologies for making sense of personal health measurement and assessment, as well as for delivering situational, actionable insights in care flows. In recent years, the proliferation of consumer and pervasive health technologies has enabled a whole new generation of sensor-based precision measurement technologies and mobile ecological momentary assessments that are able to capture patient-specific characteristics in context [3-5]. The captured physiomes (i.e., a collection of quantitative and integrated descriptions of the functional behavior of the physiological state of an individual [1]) can help detect physiological macro-phenotypes such as inflammatory response and fatigue [8], as well as critical conditions such as seizure and atrial fibrillation [6, 7]. The accumulated longitudinal records of such phonemes are also expected to capture patterns that can help distinguish individual physiological differences, e.g., being insulin-sensitive or insulin-resistant, which will make a difference in disease diagnosis and prognosis [8].