An Ensemble Learning Algorithm Based on Dynamic Voting for Targeting the Optimal Insulin Dosage in Type 1 Diabetes Management

People with type 1 diabetes (T1D) need exogenous insulin administrations several times a day. The amount of injected insulin is key for maintaining the concentration of blood glucose (BG) within a physiological safe range. According to well-established clinical guidelines, insulin dosing at mealtime...

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Veröffentlicht in:2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Jg. 2021; S. 1828 - 1831
Hauptverfasser: Noaro, Giulia, Cappon, Giacomo, Sparacino, Giovanni, Facchinetti, Andrea
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2021
Schlagworte:
Algorithms
Blood Glucose
Blood Glucose Self-Monitoring
Diabetes Mellitus, Type 1 - drug therapy
Estimation
Glucose
Heuristic algorithms
Humans
Hypoglycemic Agents - therapeutic use
Insulin - therapeutic use
Insulin Infusion Systems
Learning systems
Machine Learning
Medical treatment
Politics
ISSN:2694-0604, 2694-0604
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Zusammenfassung:People with type 1 diabetes (T1D) need exogenous insulin administrations several times a day. The amount of injected insulin is key for maintaining the concentration of blood glucose (BG) within a physiological safe range. According to well-established clinical guidelines, insulin dosing at mealtime is calculated through an empirical formula which, however, does not take advantage of the knowledge of BG trend provided in real-time by continuous glucose monitoring (CGM) sensors. To overcome suboptimal insulin dosage, we recently used machine learning techniques to build two new models, one linear and one nonlinear, which incorporate BG trend information.In this work, we propose an ensemble learning method for mealtime insulin bolus estimation based on dynamic voting, which combines the two models by taking advantage of where each alternative performs better. Being the resulting model black-box, a tool that enables its interpretability was applied to evaluate the contribution of each feature. The proposed model was trained using a synthetic dataset having information on 100 virtual subjects with different mealtime conditions, and its performance was evaluated within a simulated environment.The benefit given by the ensemble method compared to the single models was confirmed by the high time within the target glycemic range, and the trade-off reached in terms of time spent below and above this range. Moreover, the model interpretation pointed out the key role played by the information on BG dynamics in the estimation of insulin dosage.
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ISSN:2694-0604
2694-0604
DOI:10.1109/EMBC46164.2021.9630843