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Objective: Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of –omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. Methods: In this paper, we present –omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. Results: To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating –omic information into EHR. Conclusion: Big data analytics is able to address –omic and EHR data challenges for paradigm shift toward precision medicine. Significance: Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact. [ABSTRACT FROM AUTHOR]

Objective: High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. Methods: A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. Results: Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. Conclusion: Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. Significance: The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified. [ABSTRACT FROM PUBLISHER]