Abstract:
Chronic care of diabetes comes with large amounts of data concerning the self- and clinical management of the disease. In this paper, we propose to treat that information...Show MoreMetadata
Abstract:
Chronic care of diabetes comes with large amounts of data concerning the self- and clinical management of the disease. In this paper, we propose to treat that information from two different perspectives. Firstly, a predictive model of short-term glucose homeostasis relying on machine learning is presented with the aim of preventing hypoglycemic events and prolonged hyperglycemia on a daily basis. Second, data mining approaches are proposed as a tool for explaining and predicting the long-term glucose control and the incidence of diabetic complications.
Date of Conference: 10-13 November 2013
Date Added to IEEE Xplore: 09 January 2014
Electronic ISBN:978-1-4799-3163-7
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- IEEE Keywords
- Diabetes ,
- Sugar ,
- Data mining ,
- Predictive models ,
- Insulin ,
- Monitoring
- Index Terms
- Machine Learning ,
- Data Mining ,
- Machine Learning Applications ,
- Hypoglycemia ,
- Large Amount Of Data ,
- Diabetic Complications ,
- Glucose Control ,
- Prolonged Hyperglycemia ,
- Clinical Data ,
- Type 2 Diabetes Mellitus ,
- Glucose Concentration ,
- Nonlinear Function ,
- Input Variables ,
- Insulin Therapy ,
- Gaussian Process ,
- Medical Knowledge ,
- Prediction In Patients ,
- Support Vector Regression ,
- Continuous Glucose Monitoring ,
- Kernel Parameters ,
- Short-term Prediction ,
- Field Of Diabetes ,
- Knowledge Extraction ,
- Prediction Of Content ,
- Machine Learning Regression ,
- Long-term Diabetes
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Diabetes ,
- Sugar ,
- Data mining ,
- Predictive models ,
- Insulin ,
- Monitoring
- Index Terms
- Machine Learning ,
- Data Mining ,
- Machine Learning Applications ,
- Hypoglycemia ,
- Large Amount Of Data ,
- Diabetic Complications ,
- Glucose Control ,
- Prolonged Hyperglycemia ,
- Clinical Data ,
- Type 2 Diabetes Mellitus ,
- Glucose Concentration ,
- Nonlinear Function ,
- Input Variables ,
- Insulin Therapy ,
- Gaussian Process ,
- Medical Knowledge ,
- Prediction In Patients ,
- Support Vector Regression ,
- Continuous Glucose Monitoring ,
- Kernel Parameters ,
- Short-term Prediction ,
- Field Of Diabetes ,
- Knowledge Extraction ,
- Prediction Of Content ,
- Machine Learning Regression ,
- Long-term Diabetes