Computer-aided systems in the management of type I diabetes: the application of a model-based strategy

Salzsieder, Eckhard; Günther, Albrecht; Fischer, Uwe; Rutscher, A.; Thierbach, Ulrike

doi:10.1016/0169-2607(90)90103-g

Cited by 38 publications

(10 citation statements)

References 10 publications

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“…10 The Karlsburg Diabetes Management System, KADIS ® , was developed to improve the quality and cost-effectiveness of diabetes care and management for patients with type 1 or type 2 diabetes. [11][12][13][14][15] KADIS is derived from the Karlsburg model of glucose-insulin interactions, 16 and it generates an in silico copy of the individual metabolic profile of a given patient (a personalized "metabolic fingerprint") on the computer and allows testing of different therapeutic measures by interactive in silico simulation procedures aimed at quickly and safely identifying a regimen that may provide individually optimal glucose control. 13,14,17 The results of the simulation procedure are summarized as patient-focused personalized decision support (PDS) for the responsible physicians.…”

Section: Introductionmentioning

confidence: 99%

The Karlsburg Diabetes Management System: Translation from Research to eHealth Application

Salzsieder

Augstein

2011

J Diabetes Sci Technol

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Section: Introductionmentioning

confidence: 99%

The Karlsburg Diabetes Management System: Translation from Research to eHealth Application

Salzsieder

Augstein

2011

J Diabetes Sci Technol

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“…Among these advisory systems, KADIS, AIDA (Diabetic Software Simulation Program), and the Diabetes Advisory System (DIAS) are the only interactive model-based systems focused on interactive simulation of insulin/glucose profiles (9,10,12). While AIDA is a freeware computer program for educational purposes (9), DIAS can be used to predict the effects of changes in insulin dose or food intake (10).…”

mentioning

confidence: 99%

Outpatient Assessment of Karlsburg Diabetes Management System–Based Decision Support

et al. 2007

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OBJECTIVE -We sought to assess the benefit of the Karlsburg Diabetes Management System (KADIS) in conjunction with the continuous glucose monitoring system (CGMS) in an outpatient setting.RESEARCH DESIGN AND METHODS -A multicentric trial was performed in insulin-treated outpatients (n ϭ 49), aged 21-70 years, with a mean diabetes duration of 14.2 years. Subjects were recruited from five outpatient centers and randomized for CGMS-or CGMS/ KADIS-based decision support and followed up for 3 months. After two CGMS monitorings, the outcome parameters A1C (%), mean sensor glucose of the CGMS profile (MSG) (mmol/l), and duration of hyperglycemia (h/day) were evaluated.RESULTS -In contrast with the CGMS group (0.27 Ϯ 0.67%), mean change in A1C decreased in the CGMS/KADIS group during the follow-up (Ϫ0.34 Ϯ 0.49%; P Ͻ 0.01). MSG levels were not affected in the CGMS group (7.75 Ϯ 1.33 vs. 8.45 Ϯ 2.46 mmol/l) but declined in the CGMS/KADIS group (8.43 Ϯ 1.33 vs. 7.59 Ϯ 1.47 mmol/l; P Ͻ 0.05). Net KADIS effect (Ϫ0.60 [95% CI Ϫ0.96 to Ϫ 0.25%]; P Ͻ 0.01) was associated with reduced duration of hyperglycemia (4.6 vs. 1.0 h/day; P Ͻ 0.01) without increasing hypoglycemia. Multiple regression revealed that the A1C outcome was dependent on KADIS-based decision support. Age, sex, physician's specialty, diabetes type, and BMI had no measurable effect. CONCLUSIONS -If physicians were supported by CGMS/KADIS in therapeutic decisions, they achieved better glycemic control for their patients compared with support by CGMS alone. KADIS is a suitable decision support tool for physicians in outpatient diabetes care and has the potential to improve evidence-based management of diabetes.

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“…Let be the end of the th time window for grouped input We integrate the inputs in the respective time windows to form (7) The sizes of the time windows are indicated in Fig. 7 and were chosen following the consultation with a physician.…”

Section: A Hard Limited Time Windows (Tscnn1-hl)mentioning

confidence: 99%

Neural-network models for the blood glucose metabolism of a diabetic

Tresp

Briegel²,

Moody

1999

IEEE Trans. Neural Netw.

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We study the application of neural networks to modeling the blood glucose metabolism of a diabetic. In particular we consider recurrent neural networks and time series convolution neural networks which we compare to linear models and to nonlinear compartment models. We include a linear error model to take into account the uncertainty in the system and for handling missing blood glucose observations. Our results indicate that best performance can be achieved by the combination of the recurrent neural network and the linear error model.

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Computer-aided systems in the management of type I diabetes: the application of a model-based strategy

Cited by 38 publications

References 10 publications

The Karlsburg Diabetes Management System: Translation from Research to eHealth Application

The Karlsburg Diabetes Management System: Translation from Research to eHealth Application

Outpatient Assessment of Karlsburg Diabetes Management System–Based Decision Support

Neural-network models for the blood glucose metabolism of a diabetic

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