DeeDee - A mobile intelligent system able to assist a type 1 diabetic through the daily life

SACI(2014)

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摘要
Type 1 diabetes mellitus is a chronic autoimmune disorder, an incurable disease affecting somebody's entire life. Yet, with proper care, a diabetic person can have a normal life and prevent complications. We believe technology could be a valuable helping tool, providing adequate decision support to diabetics. Our idea consists in realizing an intelligent system (DeeDee system) to assist a diabetic through the daily life, implemented on a mobile phone, a device to be carried on oneself along the day. The essence of this system is a new mechanism for glycaemia prediction. Existing mechanisms for glycaemia prediction are based on patient's continuous monitoring, i.e., the patient is placed into a controlled environment (a hospital), while our system aims to predict glycaemia and hence to help the patient through his/her everyday life. Compared to a continuous monitoring situation, in our case the amount of data obtained from the patient is much less. In order to correctly predict the value of glycaemia, our system relies on similar situation from diabetic's past. A key concept needed to ensure the functionality of the entire system is the construction and the management of diabetic's profiles. Each profile is automatically built such that it characterizes and groups similar situations from diabetic's life, i.e., how the diabetic's organism has reacted in these situations. In order to group correctly the similar situations, we need the context parameters recorded at that time moments. These parameters are continuously collected by the patient's mobile phone. This article proposes an integrated system capable to communicate with other medical equipments, having one main goal: to help a diabetic to have a better life.
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关键词
type 1 diabetic assistance,diseases,type 1 diabetes mellitus,glycaemia prediction,diabetic profile construction,patient monitoring,deedee system,mobile intelligent system,diabetic profile management,assisted living,continuous patient monitoring,medical computing,mobile computing,patient mobile phone,diabetes,computational modeling,predictive models,insulin
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