Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

Muñoz-Organero, Mario

doi:10.3390/s20143896

Cited by 49 publications

(33 citation statements)

References 37 publications

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“…This random partitioning of the data into training and validation subsets and repeating the process across multiple folds is called cross-validation. Studies across the literature have used different validation strategies such as random sampling [ 20 , 65 - 67 ], time-based splitting [ 4 , 5 , 67 - 69 ], patient-specific splitting [ 6 , 32 , 53 , 70 , 71 ], or a combination of these methods to estimate predictive model performance. Simple random sampling–based cross-validation [ 72 , 73 ] may not fully address the generalizability aspect of the model to new patients and new time periods.…”

Section: Discussionmentioning

confidence: 99%

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

et al. 2021

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Background Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences. Objective This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods. Methods Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods). Results This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies. Conclusions Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.

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Section: Discussionmentioning

confidence: 99%

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

et al. 2021

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“…The number of memory units in the LSTM cells for the first layer has been set to 10 and has been reduced to 5 in the second LSTM layer. These values have been previously validated in studies such as [ 21 ]. Dropout regularization layers have also been used in order to minimize overfitting problems.…”

Section: Resultsmentioning

confidence: 99%

“…Complex machine learning models using several LSTM layers have shown better performance when predicting upcoming values of blood glucose (BG) levels than shallow models [ 3 , 18 , 19 , 21 ]. A good machine learning model will be able to compensate some inaccuracy levels in the input data.…”

Section: Resultsmentioning

confidence: 99%

“…The AIDA2 simulator constitutes a convenient dataset, since it makes the information about insulin absorption curves computed by the simulating model available, which can be used in order to validate the approach proposed in this manuscript. The AIDA2 simulator [ 12 ] has also been used in previous research studies such as [ 20 , 21 ] in order to validate blood glucose prediction algorithms. Figure 1 shows the result for a particular simulated patient for the plasma insulin levels from a single simulation in which the levels of plasma insulin are derived from the amounts and types of insulin doses taken (shown in the lower part of the figure).…”

Section: Methodsmentioning

confidence: 99%

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Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models

Muñoz-Organero

Queipo-Álvarez

García

2021

Sensors

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Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels in range (considering the calories taken and the physical exercise carried out). In recent years, machine learning models have been developed in order to help type 1 diabetes patients with their blood glucose control. These models tend to receive the insulin units used and the carbohydrate taken as inputs and generate optimal estimations for future blood glucose levels over a prediction horizon. The body glucose kinetics is a complex user-dependent process, and learning patient-specific blood glucose patterns from insulin units and carbohydrate content is a difficult task even for deep learning-based models. This paper proposes a novel mechanism to increase the accuracy of blood glucose predictions from deep learning models based on the estimation of carbohydrate digestion and insulin absorption curves for a particular patient. This manuscript proposes a method to estimate absorption curves by using a simplified model with two parameters which are fitted to each patient by using a genetic algorithm. Using simulated data, the results show the ability of the proposed model to estimate absorption curves with mean absolute errors below 0.1 for normalized fast insulin curves having a maximum value of 1 unit.

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“…Accurate glucose prediction is also vital for the early and proactive regulation of blood glucose before it drifts to undesirable levels. Therefore, numerous approaches, based on physical models or data-driven empirical models, have been proposed to predict glucose levels [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning: a novel glucose prediction framework for new subjects with type 2 diabetes

Xia

Yang

et al. 2021

Complex Intell. Syst.

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Blood glucose (BG) prediction is an effective approach to avoid hyper- and hypoglycemia, and achieve intelligent glucose management for patients with type 1 or serious type 2 diabetes. Recent studies have tended to adopt deep learning networks to obtain improved prediction models and more accurate prediction results, which have often required significant quantities of historical continuous glucose-monitoring (CGM) data. However, for new patients with limited historical dataset, it becomes difficult to establish an acceptable deep learning network for glucose prediction. Consequently, the goal of this study was to design a novel prediction framework with instance-based and network-based deep transfer learning for cross-subject glucose prediction based on segmented CGM time series. Taking the effects of biodiversity into consideration, dynamic time warping (DTW) was applied to determine the proper source domain dataset that shared the greatest degree of similarity for new subjects. After that, a network-based deep transfer learning method was designed with cross-domain dataset to obtain a personalized model combined with improved generalization capability. In a case study, the clinical dataset demonstrated that, with additional segmented dataset from other subjects, the proposed deep transfer learning framework achieved more accurate glucose predictions for new subjects with type 2 diabetes.

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Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

Cited by 49 publications

References 37 publications

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models

Deep transfer learning: a novel glucose prediction framework for new subjects with type 2 diabetes

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