title: Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes.
abstract: 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.
authors.affiliation: Department of Computer Science, Academy of Business in Dabrowa Gornicza, Cieplaka 1c, Dabrowa Gornicza, Poland. rdeja@wsb.edu.pl.
authors.forename: Rafał
authors.initials: R
authors.lastname: Deja
authors.affiliation: Institute of Computer Science, University of Silesia, Bedzinska 39, Sosnowiec, Poland. wojciech.froelich@us.edu.pl.
authors.forename: Wojciech
authors.initials: W
authors.lastname: Froelich
authors.affiliation: School of Medicine in Katowice, Department of Pediatrics, Medykow 16, Katowice, Poland. gr.deja@gmail.com.
authors.forename: Grażyna
authors.initials: G
authors.lastname: Deja
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D002940:Circadian Rhythm; D057225:Data Mining; D066264:Datasets as Topic; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy* / Q000503:physiopathology; D004305:Dose-Response Relationship, Drug; D004334:Drug Administration Schedule; D004435:Eating; D005934:Glucagon / Q000378:metabolism; D006801:Humans; D007328:Insulin / Q000008:administration & dosage / Q000378:metabolism / Q000627:therapeutic use*; D000078790:Insulin Secretion; D010179:Pancreas / Q000378:metabolism; D012636:Secretory Rate; D012890:Sleep
chemical_list: D001786:Blood Glucose; D007328:Insulin; D005934:Glucagon