Personalized glucose forecasting for type 2 diabetes using data assimilation
Publication Date
April 27, 2017
Authors
David J. Albers, Matthew Levine, Bruce Gluckman, Henry Ginsberg, et al
Volume
13
Issue
4
Pages
e1005232
DOI
https://dx.plos.org/10.1371/journal.pcbi.1005232
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1005232
Scopus
85018307645
Mendeley
http://www.mendeley.com/research/personalized-glucose-forecasting-type-2-diabetes-using-data-assimilation
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Mendeley | Further Information

{"title"=>"Personalized glucose forecasting for type 2 diabetes using data assimilation", "type"=>"journal", "authors"=>[{"first_name"=>"David J.", "last_name"=>"Albers", "scopus_author_id"=>"55341958200"}, {"first_name"=>"Matthew", "last_name"=>"Levine", "scopus_author_id"=>"57190301502"}, {"first_name"=>"Bruce", "last_name"=>"Gluckman", "scopus_author_id"=>"6603806723"}, {"first_name"=>"Henry", "last_name"=>"Ginsberg", "scopus_author_id"=>"7102877733"}, {"first_name"=>"George", "last_name"=>"Hripcsak", "scopus_author_id"=>"7004471151"}, {"first_name"=>"Lena", "last_name"=>"Mamykina", "scopus_author_id"=>"14035900100"}], "year"=>2017, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"615826457", "sgr"=>"85018307645", "pmid"=>"28448498", "scopus"=>"2-s2.0-85018307645", "isbn"=>"1111111111", "doi"=>"10.1371/journal.pcbi.1005232", "issn"=>"15537358"}, "id"=>"4df326c9-e04e-339e-ab44-4b44e5acf70d", "abstract"=>"Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.", "link"=>"http://www.mendeley.com/research/personalized-glucose-forecasting-type-2-diabetes-using-data-assimilation", "reader_count"=>36, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>7, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>7, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>6, "Biochemistry, Genetics and Molecular Biology"=>5, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>3, "Medicine and Dentistry"=>2, "Chemical Engineering"=>1, "Chemistry"=>1, "Social Sciences"=>2, "Computer Science"=>12, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>6}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>2}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>12}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Unspecified"=>{"Unspecified"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Austria"=>1}, "group_count"=>5}

Scopus | Further Information

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