{"id":6628,"date":"2017-03-31T13:31:48","date_gmt":"2017-03-31T11:31:48","guid":{"rendered":"http:\/\/medlab.kalatzis.eu\/?page_id=6628"},"modified":"2020-05-07T11:37:23","modified_gmt":"2020-05-07T08:37:23","slug":"modelling-of-glycaemic-control-in-diabetic-patients","status":"publish","type":"page","link":"https:\/\/medlab.cc.uoi.gr\/?page_id=6628","title":{"rendered":"Modelling of glycaemic control in diabetic patients"},"content":{"rendered":"<section class=\"l-section wpb_row height_auto width_full color_primary with_img\"><div class=\"l-section-img\" role=\"img\" aria-label=\"Image\" data-img-width=\"1238\" data-img-height=\"598\" style=\"background-image: url(https:\/\/medlab.cc.uoi.gr\/wp-content\/uploads\/2017\/03\/Biomaterials_26-3-2017.png);background-repeat: no-repeat;\"><\/div><div class=\"l-section-h i-cf\"><div class=\"g-cols vc_row via_flex valign_top type_default stacking_default\"><div class=\"vc_col-sm-12 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"ult-spacer spacer-6a0964c071189\" data-id=\"6a0964c071189\" data-height=\"100\" data-height-mobile=\"50\" data-height-tab=\"50\" data-height-tab-portrait=\"50\" data-height-mobile-landscape=\"50\" style=\"clear:both;display:block;\"><\/div>[vc_custom_heading source=&#8221;post_title&#8221; font_container=&#8221;tag:h1|font_size:66|text_align:center|color:%23000000&#8243; google_fonts=&#8221;font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:600%20bold%20regular%3A600%3Anormal&#8221; css=&#8221;%7B%22default%22%3A%7B%22color%22%3A%22%23000000%22%2C%22font-size%22%3A%2266%22%7D%7D&#8221;]<div class=\"w-iconbox iconpos_top style_default color_primary align_center no_text\"><a href=\"#history\" class=\"w-iconbox-link\" aria-label=\"2016-2017\"><div class=\"w-iconbox-icon\" style=\"font-size:2rem;\"><i class=\"fas fa-chevron-down\"><\/i><\/div><\/a><div class=\"w-iconbox-meta\"><h4 class=\"w-iconbox-title\"><a href=\"#history\" class=\"w-iconbox-link\" aria-label=\"2016-2017\">2016-2017<\/a><\/h4><\/div><\/div><div class=\"ult-spacer spacer-6a0964c071249\" data-id=\"6a0964c071249\" data-height=\"100\" data-height-mobile=\"50\" data-height-tab=\"50\" data-height-tab-portrait=\"50\" data-height-mobile-landscape=\"50\" style=\"clear:both;display:block;\"><\/div><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"l-section wpb_row height_auto width_full\" id=\"history\"><div class=\"l-section-h i-cf\"><div class=\"g-cols vc_row via_flex valign_top type_default stacking_default\"><div class=\"vc_col-sm-12 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"w-tabs layout_hor style_default switch_click has_scrolling\" style=\"--sections-title-size:20px\"><div class=\"w-tabs-list items_2 align_justify\" style=\"font-family:var(--font-family);\"><div class=\"w-tabs-list-h\"><button class=\"w-tabs-item active defined-active\" aria-controls=\"content-za6e\" aria-expanded=\"true\"><span class=\"w-tabs-item-title\">Introduction<\/span><\/button><button class=\"w-tabs-item\" aria-controls=\"content-ee67\" aria-expanded=\"false\"><span class=\"w-tabs-item-title\">Prediction algorithms of the subcutaneous glucose<\/span><\/button><\/div><\/div><div class=\"w-tabs-sections titles-align_none icon_chevron cpos_right\"><div class=\"w-tabs-section active\" id=\"za6e\"><button class=\"w-tabs-section-header active\" aria-controls=\"content-za6e\" aria-expanded=\"true\"><div class=\"w-tabs-section-title\">Introduction<\/div><div class=\"w-tabs-section-control\"><\/div><\/button><div  class=\"w-tabs-section-content\" id=\"content-za6e\"><div class=\"w-tabs-section-content-h i-cf\"><div class=\"g-cols wpb_row via_flex valign_top type_default stacking_default\" style=\"--additional-gap:30;\"><div class=\"vc_col-sm-12 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"wpb_text_column\"><div class=\"wpb_wrapper\"><p>The use of short-term prediction algorithms of the subcutaneous (s.c.) glucose concentration may contribute significantly to the prevention of hypoglycemic events and the daily management of insulin-treated diabetes. We have treated s.c. glucose prediction as a multivariate regression problem using support vector regression (SVR). The method is based on variables concerning: (i) the s.c. glucose profile, (ii) the plasma insulin concentration, (iii) the appearance of meal-derived glucose in the systemic circulation, and (iv) the energy expenditure during physical activities. We have also extended our SVR model to predict separately the nocturnal events during sleep and the non-nocturnal (i.e. diurnal) ones over 30-min and 60-min horizons for a hypoglycemic threshold of 70 mg\/dl. Additional variables have been introduced accounting for recurrent nocturnal hypoglycemia due to antecedent hypoglycemia, exercise and sleep. The application of RF and RreliefF feature evaluation algorithms on real-life Type 1 diabetes data has also been proposed as a means to customize the input of glucose predictive models.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"w-tabs-section\" id=\"ee67\"><button class=\"w-tabs-section-header\" aria-controls=\"content-ee67\" aria-expanded=\"false\"><div class=\"w-tabs-section-title\">Prediction algorithms of the subcutaneous glucose<\/div><div class=\"w-tabs-section-control\"><\/div><\/button><div  class=\"w-tabs-section-content\" id=\"content-ee67\"><div class=\"w-tabs-section-content-h i-cf\"><div class=\"g-cols wpb_row via_flex valign_top type_default stacking_default\"><div class=\"vc_col-sm-12 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"wpb_text_column\"><div class=\"wpb_wrapper\"><p>Modelling of human glucose metabolism in patients suffering from Diabetes Mellitus is an attractive research topic. Our aim is to develop dynamic models of the metabolic behaviour of insulin-treated diabetic patients (either type-1 or type 2) to predict the influence of specific parameters on glucose level and provide decision support to both the patient and the treating physician.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-6508\" src=\"http:\/\/medlab.kalatzis.eu\/wp-content\/uploads\/2017\/03\/diabetes-1.jpg\" alt=\"\" width=\"833\" height=\"239\" srcset=\"https:\/\/medlab.cc.uoi.gr\/wp-content\/uploads\/2017\/03\/diabetes-1.jpg 833w, https:\/\/medlab.cc.uoi.gr\/wp-content\/uploads\/2017\/03\/diabetes-1-300x86.jpg 300w, https:\/\/medlab.cc.uoi.gr\/wp-content\/uploads\/2017\/03\/diabetes-1-768x220.jpg 768w, https:\/\/medlab.cc.uoi.gr\/wp-content\/uploads\/2017\/03\/diabetes-1-600x172.jpg 600w\" sizes=\"auto, (max-width: 833px) 100vw, 833px\" \/><\/p>\n<p>Modelling human glucose metabolism in Diabetes Mellitus patients requires the development of complex algorithms and the measurement of a number of parameters. Different approaches to glucose metabolism modelling have been investigated in the literature.<\/p>\n<p>We aim to develop dynamic models of the metabolic behaviour of insulin-treated diabetic patients (either type-1 or type 2) to predict the influence of specific parameters on glucose level and provide decision support to both the patient and the treating physician. The model of the glucose metabolism of diabetic patients will provide predictions of patient\u2019s blood glucose value based on both traditional data (e.g. insulin and exogenous glucose characteristics, subcutaneous glucose concentration) and contextual patient-specific data such as dietary habits, physical activity and energy expenditure. All the available data will form a database that will be used to generate patient-specific models of the glycaemic profile. Methods based on pattern and computational intelligence, time series analysis, fuzzy modelling and data mining will be used in order to analyse the main processes affecting glucose metabolism in diabetic patients and implement a glycaemic profile regarding an individual.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"[vc_custom_heading source=\"post_title\" font_container=\"tag:h1|font_size:66|text_align:center|color:%23000000\" google_fonts=\"font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:600%20bold%20regular%3A600%3Anormal\" css=\"%7B%22default%22%3A%7B%22color%22%3A%22%23000000%22%2C%22font-size%22%3A%2266%22%7D%7D\"]2016-2017IntroductionPrediction algorithms of the subcutaneous glucoseIntroductionThe use of short-term prediction algorithms of the subcutaneous (s.c.) glucose concentration may contribute significantly to the prevention of hypoglycemic events and the daily management of insulin-treated diabetes. We have treated s.c. glucose prediction as a multivariate regression problem using support vector regression (SVR). The method...","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-6628","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=\/wp\/v2\/pages\/6628","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6628"}],"version-history":[{"count":5,"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=\/wp\/v2\/pages\/6628\/revisions"}],"predecessor-version":[{"id":9332,"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=\/wp\/v2\/pages\/6628\/revisions\/9332"}],"wp:attachment":[{"href":"https:\/\/medlab.cc.uoi.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}