About this Research Topic
This Research Topic addresses recent advances in data mining, machine learning, and predictive analytics of the growing volume of heterogeneous health data that is emerging from both real time, on-line and offline systems, and that exists in a variety of formats, types, and dimensions. The ability to capture, manage, characterize, classify, cluster, process, mine, and analyze such data represents a challenging task. Design and development of methods that enable capturing health data from IoT sensors; integrating, storing, and indexing these data; de-noising, enhancing, segmenting, and classifying the data; extracting and selecting features from the data; and transferring, sharing, and visualizing the data is essential. Key foci include security, privacy, and trust of the data and associated analytics. Also important is the governance of health data collected from and stored onmultiple, heterogeneous IoT devices, networks, platforms, and systems such as public and private clouds.
Further, this Research Topic will consider the emerging IoT ecosystem for health apps, including the design, deployment, and use of these technologies, and their associated ongoing refinement and evolution.
Keywords: eHealth, mHealth, Digital Medicine, Mobile App, IoT sensors, Wearables, Medical Data, Data Mining, Learning and Analysis, Systems Medicine, Wellness
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.