Predictive Glucose Monitoring for Type 1 Diabetes Using Latent Variable-based Multivariate Statistical Analysis

Zhao, Chunhui; Dassau, Eyal; Harvey, Rebecca A.; Seborg, Dale E.; Doyle, Francis J.

doi:10.3182/20110828-6-it-1002.02330

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…In this article, AR modeling techniques based on standard least-squares (LS) algebra and a new latent-variable-based statistical analysis 15,16 are used to develop empirical prediction models from glucose time series data. Corresponding ARX models are evaluated after adding the exogenous inputs to the AR models.…”

Section: Standard Ar and Arx Prediction Modelsmentioning

confidence: 99%

“…They evaluated the effects of key design issues such as the degree of input excitation, model orders, and prediction horizons. A new latent variable (LV)based statistical modeling algorithm has recently been developed by our research group 15,16 for T1DM applications. The results for clinical and in silico applications have demonstrated the effectiveness of the proposed method and its improved prediction accuracy, compared with standard AR and ARX models.…”

Section: Introductionmentioning

confidence: 99%

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Online prediction of subcutaneous glucose concentration for type 1 diabetes using empirical models and frequency‐band separation

et al. 2013

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Online glucose prediction which can be used to provide important information of future glucose status is a key step to facilitate proactive management before glucose reaches undesirable concentrations. Based on frequency-band separation (FS) and empirical modeling approaches, this article considers several important aspects of on-line glucose prediction for subjects with type 1 diabetes mellitus. Three issues are of particular interest: (1) Can a global (or universal) model be developed from glucose data for a single subject and then used to make suitably accurate on-line glucose predictions for other subjects? (2) Does a new FS approach based on data filtering provide more accurate models than standard modeling methods? (3) Does a new latent variable modeling method result in more accurate models than standard modeling methods? These and related issues are investigated by developing autoregressive models and autoregressive models with exogenous inputs based on clinical data for two groups of subjects. The alternative modeling approaches are evaluated with respect to on-line short-term prediction accuracy for prediction horizons of 30 and 60 min, using independent test data.

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Section: Standard Ar and Arx Prediction Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online prediction of subcutaneous glucose concentration for type 1 diabetes using empirical models and frequency‐band separation

et al. 2013

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“…To address this problem, previous research studies (Parker et al 1999, Reifman et al, Gani et al 2010, Pérez-Gandía et al 2010, Nixon and Pickup 2011, Pappada et al 2011, Zhao et al 2011, Eren-Oruklu et al 2012, Turksoy et al 2014, Zhao et al 2014, Kirchsteiger et al 2015, Zhao and Yu 2015) have utilized various types of models for BGC prediction (or even development of AP systems) that can generally be divided into two main categories: physiological models and data-driven empirical models. Physiological models describe the glucose dynamics based on the fundamental understanding of the biological and chemical phenomena.…”

Section: Introductionmentioning

confidence: 99%

Model-fusion-based online glucose concentration predictions in people with type 1 diabetes

Xia

Turksoy

Rashid

et al. 2018

Control Engineering Practice

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Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.

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“…Eren-Oruklu and coworkers [10], [11] have reported subject-specific recursive AR models where the model parameters were recursively updated to reflect the recent glucose history. A new latent variable (LV)-based statistical modelling algorithm has recently been developed [15] for T1DM applications. The results for clinical and in silico applications have demonstrated the effectiveness of the proposed method and its improved prediction accuracy, compared with standard AR and ARX models.…”

Section: Introductionmentioning

confidence: 99%

Multivariate statistical analysis methods to investigate interindividual glucose dynamics for subjects with type 1 diabetes mellitus

Zhao

Sun

Gao

2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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This paper investigates the interindividual variability of underlying glucose dynamics using multivariate statistical analysis methods for subjects with type 1 diabetes mellitus. Here two types of glucose dynamics are defined, the general dynamics and the output-relevant predictive dynamics. The concerned important issues are whether the underlying glucose dynamics change from subject to subject? Can a global (or universal) empirical model be developed from glucose data for a single subject and then used to explain the glucose dynamics for other subjects? These and related issues are investigated using multivariate statistical analysis methods based on clinical data for two groups of subjects. Together, these findings provide insights into more efficient development of data-driven models to understand and capture the glucose information in diabetes subjects.

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Predictive Glucose Monitoring for Type 1 Diabetes Using Latent Variable-based Multivariate Statistical Analysis

Cited by 6 publications

References 20 publications

Online prediction of subcutaneous glucose concentration for type 1 diabetes using empirical models and frequency‐band separation

Online prediction of subcutaneous glucose concentration for type 1 diabetes using empirical models and frequency‐band separation

Model-fusion-based online glucose concentration predictions in people with type 1 diabetes

Multivariate statistical analysis methods to investigate interindividual glucose dynamics for subjects with type 1 diabetes mellitus

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