دانلود مقاله انگلیسی رایگان:مدل سازی داده محور و پیش بینی پویایی قند خون: کاربردهای یادگیری ماشین در دیابت نوع 1 - 2019
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  • Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes
    Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes


    ترجمه فارسی عنوان مقاله:

    مدل سازی داده محور و پیش بینی پویایی قند خون: کاربردهای یادگیری ماشین در دیابت نوع 1


    منبع:

    Sciencedirect - Elsevier - Artificial Intelligence In Medicine, 98 (2019) 109-134: doi:10:1016/j:artmed:2019:07:007


    نویسنده:

    Ashenafi Zebene Woldaregaya,⁎, Eirik Årsandb, Ståle Walderhaugb,c, David Albersd, Lena Mamykinad, Taxiarchis Botsise, Gunnar Hartvigsena


    چکیده انگلیسی:

    Background: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabetes. However, self-management of the disease, especially keeping BG in the recommended range, is central to the treatment. This includes actively tracking BG levels and managing physical activity, diet, and insulin intake. The recent advancements in diabetes technologies and self-management applications have made it easier for patients to have more access to relevant data. In this regard, the development of an artificial pancreas (a closed-loop system), personalized decision systems, and BG event alarms are becoming more apparent than ever. Techniques such as predicting BG (modeling of a personalized profile), and modeling BG dynamics are central to the development of these diabetes management technologies. The increased availability of sufficient patient historical data has paved the way for the introduction of machine learning and its application for intelligent and improved systems for diabetes management. The capability of machine learning to solve complex tasks with dynamic environment and knowledge has contributed to its success in diabetes research. Motivation: Recently, machine learning and data mining have become popular, with their expanding application in diabetes research and within BG prediction services in particular. Despite the increasing and expanding popularity of machine learning applications in BG prediction services, updated reviews that map and materialize the current trends in modeling options and strategies are lacking within the context of BG prediction (modeling of personalized profile) in type 1 diabetes. Objective: The objective of this review is to develop a compact guide regarding modeling options and strategies of machine learning and a hybrid system focusing on the prediction of BG dynamics in type 1 diabetes. The review covers machine learning approaches pertinent to the controller of an artificial pancreas (closed-loop systems), modeling of personalized profiles, personalized decision support systems, and BG alarm event applications. Generally, the review will identify, assess, analyze, and discuss the current trends of machine learning applications within these contexts. Method: A rigorous literature review was conducted between August 2017 and February 2018 through various online databases, including Google Scholar, PubMed, ScienceDirect, and others. Additionally, peer-reviewed journals and articles were considered. Relevant studies were first identified by reviewing the title, keywords, and abstracts as preliminary filters with our selection criteria, and then we reviewed the full texts of the articles that were found relevant. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming among the authors. Results: The initial search was done by analyzing the title, abstract, and keywords. A total of 624 papers were retrieved from DBLP Computer Science (25), Diabetes Technology and Therapeutics (31), Google Scholar (193), IEEE (267), Journal of Diabetes Science and Technology (31), PubMed/Medline (27), and ScienceDirect (50). After removing duplicates from the list, 417 records remained. Then, we independently assessed and screened the articles based on the inclusion and exclusion criteria, which eliminated another 204 papers, leaving 213 relevant papers. After a full-text assessment, 55 articles were left, which were critically analyzed. The inter-rater agreement was measured using a Cohen Kappa test, and disagreements were resolved through discussion. Conclusion: Due to the complexity of BG dynamics, it remains difficult to achieve a universal model that produces an accurate prediction in every circumstance (i.e., hypo/eu/hyperglycemia e


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 26
    حجم فایل: 4465 کیلوبایت

    قیمت: رایگان


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