The Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) will generate generalizable theory, methods, tools, and software, to address major barriers to processing complex mobile sensor data and using it for biomedical discovery. It will develop and implement a standards-based, interoperable, extensible and open-source big data software platform for efficient implementation of MD2K data analytics. The MD2K Center will demonstrate the feasibility, utility, and generalizability of the MD2K approach by implementing the entire MD2K data analytics system in the context of two biomedical applications - reducing relapse among abstinent daily smokers and reducing readmission among congestive heart failure (CHF) patients. All tools and software developed by the Center will be freely available as standards-compliant and open-source with documentation to engage data science researchers in advancing the science of MD2K and its integration with other biomedical big data such as EHRs, genomics, and imaging. The Center will provide training materials to help biomedical researchers install the MD2K software on their mobile devices and servers to collect mobile sensor data and analyze these data for biomedical discovery. The Administration Core of the MD2K Center will establish an organizational structure, operating procedures, and managerial practices that effectively facilitate coordination, communication and collaboration among team members. It will develop quantifiable measures and implement systems to monitor, assess, and evaluate the quality and utility of MD2K products and training to measure progress and gauge the long term impact of Center activities. It will implement administrative management systems and procedures to facilitate management of the Center's financial, communications and research infrastructure, resources, and networks. It will ensure real-time access to meaningful information, data, and tools among MD2K team members, BD2K Consortium members, NIH, and interested data science and biomedical researchers via robust web tools and social media. Finally, it will explore and implement specific mechanisms for continued maintenance and distribution of software and tools to the research community beyond the funding period.
By enabling the use of mobile sensor data to detect and predict person-specific disease risk factors ahead of the onset of adverse clinical events, this project will make it possible to intervene anytime, anywhere, to anticipate and prevent disease complications at the earliest timepoint, ultimately supporting wellness. These tools, made freely available, will reduce the burden of complex chronic disorders on health and healthcare.
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