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Title: The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications
Authors: Qin C.
Tao L. 
Phang Y.H.
Zhang C. 
Chen S.Y. 
Zhang P.
Tan Y.
Jiang Y.Y.
Chen Y.Z.
Keywords: biological marker
disease management
health care delivery
information processing
mass screening
medical technology
meta analysis
molecular diagnosis
personalized medicine
Automatic Data Processing
Biomedical Technology
Delivery of Health Care
Disease Management
Mass Screening
Molecular Diagnostic Techniques
Precision Medicine
Issue Date: 2015
Citation: Qin C., Tao L., Phang Y.H., Zhang C., Chen S.Y., Zhang P., Tan Y., Jiang Y.Y., Chen Y.Z. (2015). The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications. Scientific Reports 5 : 17854. ScholarBank@NUS Repository.
Abstract: Mobile health technologies to detect physiological and simple-Analyte biomarkers have been explored for the improvement and cost-reduction of healthcare services, some of which have been endorsed by the US FDA. Advancements in the investigations of non-invasive and minimally-invasive molecular biomarkers and biomarker candidates and the development of portable biomarker detection technologies have fuelled great interests in these new technologies for mhealth applications. But apart from the development of more portable biomarker detection technologies, key questions need to be answered and resolved regarding to the relevance, coverage, and performance of these technologies and the big data management issues arising from their wide spread applications. In this work, we analyzed the newly emerging portable biomarker detection technologies, the 664 non-invasive molecular biomarkers and the 592 potential minimally-invasive blood molecular biomarkers, focusing on their detection capability, affordability, relevance, and coverage. Our analysis suggests that a substantial percentage of these biomarkers together with the new technologies can be potentially used for a variety of disease conditions in mhealth applications. We further propose a new strategy for reducing the workload in the processing and analysis of the big data arising from widespread use of mhealth products, and discuss potential issues of implementing this strategy.
Source Title: Scientific Reports
ISSN: 2045-2322
DOI: 10.1038/srep17854
Appears in Collections:Staff Publications

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