Compartmental models for glycaemic prediction and decision-support in clinical diabetes care: promise and reality

Lehmann, Eldon D.; Deutsch, Tibor

doi:10.1016/s0169-2607(98)00025-x

Cited by 59 publications

(38 citation statements)

References 39 publications

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“…22 While the simulations were shown to be unsuitable for individual patient glycemic prediction and therapy planning, they have found widespread use for educational/demonstration/self-learning purposes where individual predictive accuracy is less critical. 23,24 Using the evaluation scheme shown in Figure 1, the current report would fit under level 4 (anecdotal evidence including user comments and reviews). While this feedback has been very encouraging, the next stage in the evaluation process is clearly to undertake level 3 observational studies (including the use of surveys and more formal questionnaires 21 ) and prospective RCTs.…”

Section: Discussionmentioning

confidence: 99%

Further User Comments Regarding Usage of an Interactive Educational Diabetes Simulator (AIDA)

Lehmann

2002

Diabetes Technology & Therapeutics

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This "Diabetes Information Technology & WebWatch" column continues the diabetes simulation theme from previous issues and overviews various user experience with the AIDA v4 interactive educational freeware diabetes simulator. AIDA is a diabetes computer program that permits the interactive simulation of plasma insulin and blood glucose (BG) profiles for educational, demonstration, and self-learning purposes. It has been made freely available, without charge, via the Web as a noncommercial contribution to continuing diabetes education. Since its Internet launch in 1996, over 145,000 visits have been logged at the AIDA Website-www.2aida.org-and over 29,000 copies of the program have been downloaded, free of charge. While these statistics may appear impressive, they do not tell the personal story of how people have been making use of the software, and what they actually think about the program. In this respect, this column documents some of the independent user comments about AIDA sent in spontaneously via electronic mail (email) by patients with diabetes and their relatives, as well as by health-care professionals. Comments posted to diabetes newsgroups and diabetes email lists, as well as a selection of those which have been found at other, linked, diabetes Websites are also highlighted. 121

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Section: Discussionmentioning

confidence: 99%

Further User Comments Regarding Usage of an Interactive Educational Diabetes Simulator (AIDA)

Lehmann

2002

Diabetes Technology & Therapeutics

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“…For example, equations have been introduced in the literature for tracking the carbohydrate intake as it is converted to blood glucose which then interacts with the kidneys, liver, muscles, and other body systems. Most physiological models characterize the overall dynamics into three compartments: meal absorption dynamics, insulin dynamics, and glucose dynamics [6], [7], [8]. Since they are based on the same data, the equations used in the literature to model the underlying processes are almost identical [8], [9].…”

Section: Physiological Modelmentioning

confidence: 99%

Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression

Bunescu

Struble

Marling

et al. 2013

2013 12th International Conference on Machine Learning and Applications

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Abstract-Patients with diabetes must continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious short-term and long-term complications. An automatic prediction model that warned people of imminent changes in their blood glucose levels would enable them to take preventive action. Modeling inter-patient differences and the combined effects of insulin and life events on blood glucose have been particularly challenging in the design of accurate blood glucose forecasting systems. In this paper, we describe a solution that uses a generic physiological model of blood glucose dynamics to generate informative features for a Support Vector Regression model that is trained on patient specific data. Experimental results show that the new prediction model outperforms all three diabetes experts involved in the study, thus demonstrating the utility of using the generic physiological features in machine learning models that are individually trained for every patient.

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“…A physiological-based compartmental model has the advantage that the simulations can yield insight into the physiological parameters (Erzen, et al;. Such a model offers a powerful tool for generating predictions and clinical decision support in diabetes care (Lehmann, & Deutsch, 1998;Halim et al;1993). Here, a physiological-based compartmental model is used to design and test the tracking control.…”

Section: Blood Glucose Control Problemmentioning

confidence: 99%

A Robust Approach to Control Blood Glucose Level: Diabetes Mellitus Type I

Ruı́z-Velázquez

Campos-Delgado

Femat

2003

IFAC Proceedings Volumes

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Compartmental models for glycaemic prediction and decision-support in clinical diabetes care: promise and reality

Cited by 59 publications

References 39 publications

Further User Comments Regarding Usage of an Interactive Educational Diabetes Simulator (AIDA)

Further User Comments Regarding Usage of an Interactive Educational Diabetes Simulator (AIDA)

Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression

A Robust Approach to Control Blood Glucose Level: Diabetes Mellitus Type I

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