Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes

In spite of numerous research efforts on supporting the therapy of diabetes mellitus, the subject still involves challenges and creates active interest among researchers. In this paper, a decision support tool is presented for setting insulin therapy in new-onset type 1 diabetes. The concept of diff...

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Published in:Biomedical engineering online 2015-02, Vol.14 (1), p.13-13, Article 13
Main Authors: Deja, Rafał, Froelich, Wojciech, Deja, Grażyna
Format: Article
Language:English
Subjects:
Algorithms
Blood Glucose - analysis
Circadian Rhythm
Data Mining
Datasets as Topic
Decision Support Systems, Clinical
Decision Support Techniques
Diabetes Mellitus, Type 1 - blood
Diabetes Mellitus, Type 1 - drug therapy
Diabetes Mellitus, Type 1 - physiopathology
Dose-Response Relationship, Drug
Drug Administration Schedule
Eating
Glucagon - metabolism
Humans
Insulin - administration & dosage
Insulin - metabolism
Insulin - therapeutic use
Insulin Secretion
Pancreas - metabolism
Secretory Rate
Sleep
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Summary:In spite of numerous research efforts on supporting the therapy of diabetes mellitus, the subject still involves challenges and creates active interest among researchers. In this paper, a decision support tool is presented for setting insulin therapy in new-onset type 1 diabetes. The concept of differential sequential patterns (DSPs) is introduced with the aim of representing deviations in the patient's blood glucose level (BGL) and the amount of insulin injections administered. The decision support tool is created using data mining algorithms for discovering sequential patterns. By using the DSPs, it is possible to support the physician's decisionmaking concerning changing the treatment (i.e., whether to increase or decrease the insulin dosage). The other contributions of the paper are an algorithm for generating DSPs and a new method for evaluating nocturnal glycaemia. The proposed qualitative evaluation of nocturnal glycaemia improves the generalization capabilities of the DSPs. The usefulness of the proposed approach was evident in the results of experiments in which juvenile diabetic patients actual data were used. It was confirmed that the proposed DSPs can be used to guide the therapy of numerous juvenile patients with type 1 diabetes.
ISSN:1475-925X
1475-925X
DOI:10.1186/s12938-015-0004-x