{"title":"移动数据采集用于情境风险评估,以更好地管理糖尿病疾病","authors":"C. Cernazanu-Glavan, D. Lungeanu, S. Holban","doi":"10.1109/SACI.2014.6840088","DOIUrl":null,"url":null,"abstract":"Diabetes is one of the serious chronic medical conditions for which the patient's personal involvement into an adequate management of the disease is essential. This paper presents a framework to complement existing approaches in diabetes care through information technology instruments. Existing tools have been targeted at controlling the blood glucose level and generating alerts against hypo/hyper-glycaemia events, individualizing risk prediction based on epidemiological knowledge, or improving patient's education and motivation. We propose analyzing contextual data from patients, i.e. using data collected through mobile devices, on a daily basis, to uncover the implications of the life style and individual behaviour towards the risk for diabetic complications. The paper presents the rationale and the framework for this approach, rather than results from an accomplished work.","PeriodicalId":163447,"journal":{"name":"2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Mobile data acquisition towards contextual risk assessment for better disease management in diabetes\",\"authors\":\"C. Cernazanu-Glavan, D. Lungeanu, S. Holban\",\"doi\":\"10.1109/SACI.2014.6840088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diabetes is one of the serious chronic medical conditions for which the patient's personal involvement into an adequate management of the disease is essential. This paper presents a framework to complement existing approaches in diabetes care through information technology instruments. Existing tools have been targeted at controlling the blood glucose level and generating alerts against hypo/hyper-glycaemia events, individualizing risk prediction based on epidemiological knowledge, or improving patient's education and motivation. We propose analyzing contextual data from patients, i.e. using data collected through mobile devices, on a daily basis, to uncover the implications of the life style and individual behaviour towards the risk for diabetic complications. The paper presents the rationale and the framework for this approach, rather than results from an accomplished work.\",\"PeriodicalId\":163447,\"journal\":{\"name\":\"2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SACI.2014.6840088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI.2014.6840088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mobile data acquisition towards contextual risk assessment for better disease management in diabetes
Diabetes is one of the serious chronic medical conditions for which the patient's personal involvement into an adequate management of the disease is essential. This paper presents a framework to complement existing approaches in diabetes care through information technology instruments. Existing tools have been targeted at controlling the blood glucose level and generating alerts against hypo/hyper-glycaemia events, individualizing risk prediction based on epidemiological knowledge, or improving patient's education and motivation. We propose analyzing contextual data from patients, i.e. using data collected through mobile devices, on a daily basis, to uncover the implications of the life style and individual behaviour towards the risk for diabetic complications. The paper presents the rationale and the framework for this approach, rather than results from an accomplished work.