Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov analysis of patients developing sepsis using clusters
KR4HC'10 Proceedings of the ECAI 2010 conference on Knowledge representation for health-care
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The prevalence of diabetes is increasing worldwide. Despite the advances in evidence based therapies, patients with diabetes continue to encounter ongoing morbidity and diminished health-related quality of life. One of the reasons for the diminished benefit from therapy is medication noncompliance. Considerable evidence shows that a combination of therapeutic lifestyle changes (increased exercise and diet modification) and drug treatment can control and, if detected early enough, even prevent the development of diabetes and its harmful effects on health. However, despite the fact that type-2 diabetes is treatable and reversible with appropriate management, patients frequently do not comply with treatment recommendations. In this paper, we use a combination of Expectation Maximization (EM) clustering and Artificial Neural Network (ANN) modeling to determine factors influencing compliance rates, as measured in terms of medication possession ratio (MPR), among patients prescribed fixed dose combination therapy for type 2 diabetes.