A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

BACKGROUND: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. METHODS: We introduce explainable machine learning to make predictions of hypoglycemia (270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. RESULTS: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. CONCLUSIONS: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications

Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review

For individuals affected by Type 1 diabetes (T1D), a chronic disease in which the pancreas does not produce any insulin, maintaining the blood glucose (BG) concentration as much as possible within the safety range (70-180\ua0mg/dl) allows avoiding short- and long-term complications. The tuning of exogenous insulin infusion can be difficult, especially because of the inter- and intra-day variability of physiological and behavioral factors. Continuous glucose monitoring (CGM) sensors, which monitor glucose concentration in the subcutaneous tissue almost continuously, allowed improving the detection of critical hypo- and hyper-glycemic episodes. Moreover, their integration with insulin pumps for continuous subcutaneous insulin infusion allowed developing algorithms that automatically tune insulin dosing based on CGM measurements in order to mitigate the incidence of critical episodes. In this work, we aim at reviewing the literature on methods for CGM-based automatic attenuation or suspension of basal insulin with a focus on algorithms, their implementation in commercial devices and clinical evidence of their effectiveness and safety

Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning

People with diabetes have to manage their blood glucose level to keep it within an appropriate range. Predicting whether future glucose values will be outside the healthy threshold is of vital importance in order to take corrective actions to avoid potential health damage. In this paper we describe our research with the aim of predicting the future behavior of blood glucose levels, so that hypoglycemic events may be anticipated. The approach of this work is the application of transformation functions on glucose time series, and their use in convolutional neural networks. We have tested our proposed method using real data from 4 different diabetes patients with promising results.Comment: 7 pages, 7 figures, 3 table

Gramáticas evolutivas para la predicción de hipoglucemias en diabetes

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022. https://github.com/ABSysGroup/jeco/tree/TFG_GE4HYPODiabetic patients have to manage their blood sugar correctly to prevent complications. One such complication is hypoglycemia or low blood sugar, which occurs when the blood glucose concentration goes below a certain threshold. A hypoglycemic episode needs to be rectified before it becomes harmful and can be a very distressing situation for the patient. The main goal of this study is to program Structured Grammatical Evolution and Dynamic Structured Grammatical Evolution algorithms and use them to generate models for the prediction of hypoglycemic episodes in patients with diabetes. The algorithms will be used to obtain a white-box model made up of if-then-else statements that given some input data, comprised of the blood glucose and exercise readings of the patients from the previous 2 hours, optimizes a logical relation between these variables. The resulting formula will be able to determine if the patient is going to have a hypoglycemic episode in a 30, 60, 90 and 120 minutes prediction horizon.Los pacientes con diabetes deben controlar correctamente su nivel de azúcar en sangre para evitar complicaciones. Una de estas complicaciones es la hipoglucemia o nivel bajo de azúcar en sangre, que ocurre cuando la concentración de glucosa en la sangre cae por debajo de cierto umbral. Un episodio de hipoglucemia debe corregirse antes de que se vuelva dañino y puede ser una situación muy angustiosa para el paciente. El objetivo principal de este estudio es programar los algoritmos de Gramáticas Evolutivas Estructuradas y Gramáticas Evolutivas Estructuradas Dinámicas, y utilizarlos para generar modelos de predicción de episodios hipoglucémicos en pacientes con diabetes. Los algoritmos se utilizarán para obtener un modelo de caja blanca formado por sentencias if-then-else que, dados unos datos de entrada, compuestos por el nivel de la glucosa en sangre y las lecturas de datos de ejercicio de los pacientes de las 2 horas anteriores, optimiza una relación lógica entre estas variables. La fórmula resultante se usa para determinar si el paciente va a tener un episodio de hipoglucemia en un plazo de 30, 60, 90 y 120 minutos.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas

[EN] Background: Linear empirical dynamic models have been widely used for glucose prediction. The extension of the concept of seasonality, characteristic of other domains, is explored here for the improvement of prediction accuracy. Methods: Twenty time series of 8-hour postprandial periods (PP) for a same 60g-carbohydrate meal were collected from a closed-loop controller validation study. A single concatenated time series was produced representing a collection of data from similar scenarios, resulting in seasonality. Variability in the resulting time series was representative of worstcase intrasubject variability. Following a leave-one-out cross-validation, seasonal and nonseasonal autoregressive integrated moving average models (SARIMA and ARIMA) were built to analyze the effect of seasonality in the model prediction accuracy. Further improvement achieved from the inclusion of insulin infusion rate as exogenous variable was also analyzed. Prediction horizons (PHs) from 30 to 300 min were considered. Results: SARIMA outperformed ARIMA revealing a significant role of seasonality. For a 5-h PH, average MAPE was reduced in 26.62%. Considering individual runs, the improvement ranged from 6.3% to 54.52%. In the best-performing case this reduction amounted to 29.45%. The benefit of seasonality was consistent among different PHs, although lower PHs benefited more, with MAPE reduction over 50% for PHs of 60 and 120 minutes, and over 40% for 180 min. Consideration of insulin infusion rate into the seasonal model further improved performance, with a 61.89% reduction in MAPE for 30-min PH and reductions over 20% for PHs over 180 min. Conclusions: Seasonality improved model accuracy allowing for the extension of the PH significantly.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Spanish Ministry of Economy and Competitiveness, grants DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R, and the European Union through FEDER funds.Montaser Roushdi Ali, E.; Diez, J.; Bondía Company, J. (2017). Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas. Journal of Diabetes Science and Technology. 11(6):1124-1131. https://doi.org/10.1177/1932296817736074S1124113111

Utility of big data in predicting short-term blood glucose levels in type 1 diabetes mellitus through machine learning techniques

Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.The authors would like to thank to the Endocrinology Department of the Morales Meseguer and Virgen de la Arrixaca hospitals of the city of Murcia (Spain). This work was sponsored by the Spanish Ministry of Economy and Competitiveness through 387 the PERSEIDES (ref. TIN2017-86885-R) and CHIST-ERA (ref. PCIN-2016-010) projects; by MINECO grant BES-2015-071956, and by the European Comission through the H2020-ENTROPY-649849 EU Project