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10.1109/TBME.2015.2470521 http://scihub22266oqcxt.onion/10.1109/TBME.2015.2470521 C5859570!5859570!26292334     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Biomed+Eng 2015 ; 62 (12): 2735-49 Nephropedia Template TP {{tp|p=26292334|t=2015. A Review of Emerging Technologies for the Management of Diabetes Mellitus |pdf=|usr=}}{{26292334}} gab.com Text A Review of Emerging Technologies for the Management of Diabetes Mellitus JO: IEEE Trans Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=26292334 #FOAvip 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. Twit Text FOAVip A Review of Emerging Technologies for the Management of Diabetes Mellitus ????IEEE Trans Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=26292334 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26292334 #FOAvip English Wikipedia <ref>{{Cite pmid|26292334}}</ref> A Review of Emerging Technologies for the Management of Diabetes Mellitus #MMPMID26292334 Zarkogianni K; Litsa E; Mitsis K; Wu PY; Kaddi CD; Cheng CW; Wang MD; Nikita KS IEEE Trans Biomed Eng 2015[Dec]; 62 (12): 2735-49 PMID26292334show ga 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. äA Review of Emerging Technologies for the Management of Diabetes Melli(epmc).pdf DeepDyve Pubget Overpricing