Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus

Saiti, Kyriaki; Macaš, Martin; Lhotska, Lenka; Stechova, Katerina; Piťhová, Pavlína

doi:10.1016/j.cmpb.2020.105628

Cited by 32 publications

(8 citation statements)

References 24 publications

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“…Flash glucose monitoring is one such modern technology, which delivers comprehensive glucose data when an interstitial glucose sensor is scanned by handheld device. Recently, ensemble methods have emerged as potential solutions for the glucose levels prediction in T1DM patients [187]. The BG prediction involves forecasting a patient's BG levels based on past and current history (i.e., accumulated data).…”

Section: Gm Methodsmentioning

confidence: 99%

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

Mehmood

Ahmad

Arif

et al. 2020

ASI

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This paper presents a comprehensive survey about the fundamental components of the artificial pancreas (AP) system including insulin administration and delivery, glucose measurement (GM), and control strategies/algorithms used for type 1 diabetes mellitus (T1DM) treatment and control. Our main focus is on the T1DM that emerges due to pancreas’s failure to produce sufficient insulin due to the loss of beta cells (β-cells). We discuss various insulin administration and delivery methods including physiological methods, open-loop, and closed-loop schemes. Furthermore, we report several factors such as hyperglycemia, hypoglycemia, and many other physical factors that need to be considered while infusing insulin in human body via AP systems. We discuss three prominent control algorithms including proportional-integral- derivative (PID), fuzzy logic, and model predictive, which have been clinically evaluated and have all shown promising results. In addition, linear and non-linear insulin infusion control schemes have been formally discussed. To the best of our knowledge, this is the first work which systematically covers recent developments in the AP components with a solid foundation for future studies in the T1DM field.

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Section: Gm Methodsmentioning

confidence: 99%

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

Mehmood

Ahmad

Arif

et al. 2020

ASI

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“…In a literature search, [8], [9], [10], [36], presented hybrid predictive models with PHs from 90 to 120 minutes. Nevertheless, for [37], [38], [39], [40], [41], [16], [42], [43], [44], [45], [46], the prediction horizon (PH) varies from 30 to 60 minutes.…”

Section: Related Workmentioning

confidence: 99%

A Multi-agent Approach to Predict Long-Term Glucose Oscillation in Individuals with Type 1 Diabetes

Pereira¹,

Brandão²,

Bevilacqua³

et al. 2022

Preprint

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The glucose-insulin regulatory system and its glucose oscillations is a recurring theme in the literature because of its impact on human lives, mostly the ones affected by diabetes mellitus. Several approaches were proposed, from mathematical to data-based models, with the aim of modeling the glucose oscillation curve. Having such a curve, it is possible to predict, when injecting insulin in type 1 diabetes (T1D) individuals. However, the literature presents prediction horizons no longer than 6 hours, which could be a problem considering their sleeping time. This work presents Tesseratus, a model that adopts a multi-agent approach to combine machine learning and mathematical modeling to predict the glucose oscillation up to 8 hours. Tesseratus uses the pharmacokinetics of insulins and data collected from T1D individuals. Its outcome can support endocrinologists while prescripting daily treatment for T1D individuals, and provide personalized recommendations for such individuals, to keep their glucose concentration in the ideal range. Tesseratus brings pioneering results for prediction horizons of 8 hours for nighttime, in an experiment with seven real T1D individuals. It is our claim that Tesseratus will be a reference for classification of glucose prediction model, supporting the mitigation of short- and long-term complications in the T1D individuals.

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“…Saiti et al [28] evaluated ensemble algorithms: linear, bagging and boosting meteregressor to show that they performed better than the individual component models for BGL prediction. Ma et al [29] combined the residual compensation network (RCN) and the autoregressive moving average (ARMA) model for predicting BGLs in the horizon of 30 minutes and 60 minutes.…”

Section: Introductionmentioning

confidence: 99%

Development of Ensemble Tree Models for Generalized Blood Glucose Level Prediction

Aashima¹,

Bhargav²,

Kaushik³

et al. 2022

Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering

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Type-1 diabetes (T1D) patients must carefully monitor their insulin doses to avoid serious health complications. An effective regimen can be designed by predicting accurate blood glucose levels (BGLs). Several physiological and datadriven models for BGL prediction have been designed. However, less is known on the combination of different traditional machine learning (ML) algorithms for BGL prediction. Furthermore, most of the available models are patient-specific. This research aims to evaluate several traditional ML algorithms and their novel combinations for generalized BGL prediction. The data of forty T1D patients were generated using the Automated Insulin Dosage Advisor (AIDA) simulator. The twenty-four hour timeseries contained samples at fifteen-minute intervals. The training data was obtained by joining eighty percent of each patient's time-series, and the remaining twenty percent time-series was joined to obtain the testing data. The models were trained using multiple patients' data so that they could make predictions for multiple patients. The traditional non-ensemble algorithms: linear regression (LR), support vector regression (SVR), knearest neighbors (KNN), multi-layer perceptron (MLP), decision tree (DCT), and extra tree (EXT) were evaluated for forecasting BGLs of multiple patients. A new ensemble, called the Tree-SVR model, was developed. The BGL predictions from the DCT and the EXT models were fed as features into the SVR model to obtain the final outcome. The ensemble approach used in this research was based on the stacking technique. The Tree-SVR model outperformed the non-ensemble models (LR, SVR, KNN, MLP, DCT, and EXT) and other novel Tree variants (Tree-LR, Tree-MLP, and Tree-KNN). This research highlights the utility of designing ensembles using traditional ML algorithms for generalized BGL prediction.

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Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus

Cited by 32 publications

References 24 publications

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

A Multi-agent Approach to Predict Long-Term Glucose Oscillation in Individuals with Type 1 Diabetes

Development of Ensemble Tree Models for Generalized Blood Glucose Level Prediction

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