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

BioMedical Engineering OnLine

Rafał Deja²  Wojciech Froelich¹  GraŻyna Deja³ 

[1] Institute of Computer Science, University of Silesia, Bedzinska 39, Sosnowiec, Poland

[2] Department of Computer Science, Academy of Business in Dabrowa Gornicza, Cieplaka 1c, Dabrowa Gornicza, Poland

[3] School of Medicine in Katowice, Department of Pediatrics, Medykow 16, Katowice, Poland

关键词: Diabetes mellitus;    Medical patterns;    Data mining;   

Others  :  1137090
DOI  :  10.1186/s12938-015-0004-x

 received in 2014-11-02, accepted in 2015-01-26,  发布年份 2015

【 摘 要 】

Background

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.

Methods

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2015 Deja et al.; licensee BioMed Central.

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