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Medical Healthcare Network Platform and Big Data Analysis Based on Integrated ICT and Data Science with Regulatory Science
Ryuji KOHNO Takumi KOBAYASHI Chika SUGIMOTO Yukihiro KINJO Matti HÄMÄLÄINEN Jari IINATTI
IEICE TRANSACTIONS on Communications
Publication Date: 2019/06/01
Online ISSN: 1745-1345
Type of Manuscript: INVITED PAPER (Special Section on Healthcare, Medical Information and Communication Technology for Safe and Secure Society)
medical ICT, body area network (BAN), medical healthcare, big data, machine learning, dependability, regulatory science,
Full Text: FreePDF(4.3MB)
This paper provides perspectives for future medical healthcare social services and businesses that integrate advanced information and communication technology (ICT) and data science. First, we propose a universal medical healthcare platform that consists of wireless body area network (BAN), cloud network and edge computer, big data mining server and repository with machine learning. Technical aspects of the platform are discussed, including the requirements of reliability, safety and security, i.e., so-called dependability. In addition, novel technologies for satisfying the requirements are introduced. Then primary uses of the platform for personalized medicine and regulatory compliance, and its secondary uses for commercial business and sustainable operation are discussed. We are aiming at operate the universal medical healthcare platform, which is based on the principle of regulatory science, regionally and globally. In this paper, trials carried out in Kanagawa, Japan and Oulu, Finland will be revealed to illustrate a future medical healthcare social infrastructure by expanding it to Asia-Pacific, Europe and the rest of the world. We are representing the activities of Kanagawa medical device regulatory science center and a joint proposal on security in the dependable medical healthcare platform. Novel schemes of ubiquitous rehabilitation based on analyses of the training effect by remote monitoring of activities and machine learning of patient's electrocardiography (ECG) with a neural network are proposed and briefly investigated.