{"title":"胰岛素-膳食效应联合神经模糊模型预测1型糖尿病患者血糖水平","authors":"N. O. Orieke, O. Asaolu, T. Fashanu, O. Fasanmade","doi":"10.2478/ast-2019-0001","DOIUrl":null,"url":null,"abstract":"Abstract Diabetes Mellitus is a metabolic disorder that affects the ability of the human body to properly utilize and regulate glucose. It is pervasive world-wide yet tenuous and costly to manage. Diabetes Mellitus is also difficult to model because it is nonlinear, dynamic and laden with mostly patient specific uncertainties. A neuro-fuzzy model for the prediction of blood glucose level in Type 1 diabetic patients using coupled insulin and meal effects is developed. This study establishes that the necessary and sufficient conditions to predict blood glucose level in a Type 1 diabetes mellitus patient are: knowledge of the patient’s insulin effects and meal effects under diverse metabolic scenarios and the transparent coupling of the insulin and meal effects. The neuro-fuzzy models were trained with data collected from a single Type 1 diabetic patient covering a period of two months. Clarke’s Error Grid Analysis (CEGA) of the model shows that 87.5% of the predictions fall into region A, while the remaining 12.5% of the predictions fall into region B within a four (4) hour prediction window. The model reveals significant variation in insulin and glucose responses as the Body Mass Index (BMI) of the patient changes.","PeriodicalId":7998,"journal":{"name":"Annals of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Coupled Insulin and Meal Effect Neuro-Fuzzy Model for The Prediction of Blood Glucose Level in Type 1 Diabetes Mellitus Patients.\",\"authors\":\"N. O. Orieke, O. Asaolu, T. Fashanu, O. Fasanmade\",\"doi\":\"10.2478/ast-2019-0001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Diabetes Mellitus is a metabolic disorder that affects the ability of the human body to properly utilize and regulate glucose. It is pervasive world-wide yet tenuous and costly to manage. Diabetes Mellitus is also difficult to model because it is nonlinear, dynamic and laden with mostly patient specific uncertainties. A neuro-fuzzy model for the prediction of blood glucose level in Type 1 diabetic patients using coupled insulin and meal effects is developed. This study establishes that the necessary and sufficient conditions to predict blood glucose level in a Type 1 diabetes mellitus patient are: knowledge of the patient’s insulin effects and meal effects under diverse metabolic scenarios and the transparent coupling of the insulin and meal effects. The neuro-fuzzy models were trained with data collected from a single Type 1 diabetic patient covering a period of two months. Clarke’s Error Grid Analysis (CEGA) of the model shows that 87.5% of the predictions fall into region A, while the remaining 12.5% of the predictions fall into region B within a four (4) hour prediction window. The model reveals significant variation in insulin and glucose responses as the Body Mass Index (BMI) of the patient changes.\",\"PeriodicalId\":7998,\"journal\":{\"name\":\"Annals of Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/ast-2019-0001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/ast-2019-0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Coupled Insulin and Meal Effect Neuro-Fuzzy Model for The Prediction of Blood Glucose Level in Type 1 Diabetes Mellitus Patients.
Abstract Diabetes Mellitus is a metabolic disorder that affects the ability of the human body to properly utilize and regulate glucose. It is pervasive world-wide yet tenuous and costly to manage. Diabetes Mellitus is also difficult to model because it is nonlinear, dynamic and laden with mostly patient specific uncertainties. A neuro-fuzzy model for the prediction of blood glucose level in Type 1 diabetic patients using coupled insulin and meal effects is developed. This study establishes that the necessary and sufficient conditions to predict blood glucose level in a Type 1 diabetes mellitus patient are: knowledge of the patient’s insulin effects and meal effects under diverse metabolic scenarios and the transparent coupling of the insulin and meal effects. The neuro-fuzzy models were trained with data collected from a single Type 1 diabetic patient covering a period of two months. Clarke’s Error Grid Analysis (CEGA) of the model shows that 87.5% of the predictions fall into region A, while the remaining 12.5% of the predictions fall into region B within a four (4) hour prediction window. The model reveals significant variation in insulin and glucose responses as the Body Mass Index (BMI) of the patient changes.