title: An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models.
abstract: BACKGROUND
  Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.
  
  
  METHODS
  The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models' outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS.
  
  
  RESULTS
  The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms.
  
  
  CONCLUSIONS
  Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.
authors.affiliation: Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern, Switzerland.
authors.forename: Elena
authors.initials: E
authors.lastname: Daskalaki
authors.forename: Kirsten
authors.initials: K
authors.lastname: Nørgaard
authors.forename: Thomas
authors.initials: T
authors.lastname: Züger
authors.forename: Aikaterini
authors.initials: A
authors.lastname: Prountzou
authors.forename: Peter
authors.initials: P
authors.lastname: Diem
authors.forename: Stavroula
authors.initials: S
authors.lastname: Mougiakakou
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000517:prevention & control*; D007003:Hypoglycemia / Q000517:prevention & control*; D007332:Insulin Infusion Systems; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D055815:Young Adult
chemical_list: D001786:Blood Glucose