Incorporating Sparse and Quantized Carbohydrates Suggestions in Model Predictive Control for Artificial Pancreas in Type 1 Diabetes

Pavan, Jacopo; Salvagnin, Domenico; Facchinetti, Andrea; Sparacino, Giovanni; Favero, Simone Del

doi:10.1109/tcst.2022.3195072

Cited by 6 publications

(2 citation statements)

References 65 publications

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“…The automated computation of dose adjustment would leave only the residual risk associated with non-compliance. To minimize this risk, following [42], future work will investigate the inclusion in an MPC framework of quantized suggestion and the reduction in the number of therapy changes.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin

Faggionato

Guazzo

Pegolo

et al. 2023

IEEE Trans. Biomed. Eng.

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Effective dosing of anticoagulants aims to prevent blood clot formation while avoiding hemorrhages. This complex task is challenged by several disturbing factors and drugeffect uncertainties, requesting frequent monitoring and adjustment. Biovariability in drug absorption and action further complicates titration and calls for individualized strategies adapting over time. In this paper, we propose an adaptive closed-loop control algorithm to assist in warfarin therapy management. Methods: The controller was designed and tested in silico using an established pharmacometrics model of warfarin, which accounts for inter-subject variability. The control algorithm is an adaptive Model Predictive Control (a-MPC) that leverages a simplified patient model, whose parameters are updated with a Bayesian strategy. Performance was quantitatively evaluated in simulations performed on a population of virtual subjects against an algorithm reproducing medical guidelines (MG) and an MPC controller available in the literature (l-MPC). Results: The proposed a-MPC significantly (p < 0.05) lowers rising time (2.8 vs. 4.4 and 11.2 days) and time out of range (3.3 vs. 7.2 and 12.9 days) with respect to both MG and l-MPC, respectively. Adaptivity grants a significantly (p < 0.05) lower number of subjects reaching unsafe INR values compared to when this feature is not present (8.9% vs.15% of subjects presenting an overshoot outside the target range and 0.08% vs. 0.28% of subjects reaching dangerous INR values). Conclusion:The a-MPC algorithm was able to improve warfarin therapy with respect to the benchmark therapies.Significance: This in-silico validation proves the effectiveness of the a-MPC algorithm for anticoagulant administration, paving the way for clinical testing.

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Section: Discussionmentioning

confidence: 99%

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin

Faggionato

Guazzo

Pegolo

et al. 2023

IEEE Trans. Biomed. Eng.

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“…Quadratic cost functions of this type have been widely employed in artificial pancreas control algorithms to modulate temporary basal insulin rate 54 – 56 . Of note, the clinical safety and efficacy of the so-formulated minimization problem has been extensively proved for artificial pancreas systems over the last fifteen years 57 – 59 .…”

Section: Methodsmentioning

confidence: 99%

The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP

Prendin,

Pavan,

Cappon

et al. 2023

Sci Rep

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Machine learning has become a popular tool for learning models of complex dynamics from biomedical data. In Type 1 Diabetes (T1D) management, these models are increasingly been integrated in decision support systems (DSS) to forecast glucose levels and provide preventive therapeutic suggestions, like corrective insulin boluses (CIB), accordingly. Typically, models are chosen based on their prediction accuracy. However, since patient safety is a concern in this application, the algorithm should also be physiologically sound and its outcome should be explainable. This paper aims to discuss the importance of using tools to interpret the output of black-box models in T1D management by presenting a case-of-study on the selection of the best prediction algorithm to integrate in a DSS for CIB suggestion. By retrospectively “replaying” real patient data, we show that two long-short term memory neural networks (LSTM) (named p-LSTM and np-LSTM) with similar prediction accuracy could lead to different therapeutic decisions. An analysis with SHAP—a tool for explaining black-box models’ output—unambiguously shows that only p-LSTM learnt the physiological relationship between inputs and glucose prediction, and should therefore be preferred. This is verified by showing that, when embedded in the DSS, only p-LSTM can improve patients’ glycemic control.

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Personalized hybrid artificial pancreas using unidirectional sliding-modes control algorithm

Orozco-López

Castaneda

García-Sáez

et al. 2022

Biocybernetics and Biomedical Engineering

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Incorporating Sparse and Quantized Carbohydrates Suggestions in Model Predictive Control for Artificial Pancreas in Type 1 Diabetes

Cited by 6 publications

References 65 publications

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin

The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP

Personalized hybrid artificial pancreas using unidirectional sliding-modes control algorithm

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