Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes

BACKGROUND Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. OBJECTIVE This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. METHODS A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. RESULTS The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. CONCLUSIONS Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.

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A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

Applied Sciences ◽

10.3390/app11041742 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1742

Author(s):

Ignacio Rodríguez-Rodríguez ◽

José-Víctor Rodríguez ◽

Wai Lok Woo ◽

Bo Wei ◽

Domingo-Javier Pardo-Quiles

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Type 1 Diabetes ◽

Feature Selection ◽

Blood Glucose ◽

Type 1 Diabetes Mellitus ◽

Support Vector ◽

Chronic Hyperglycemia ◽

Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

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Influence of initial insulin dosage on blood glucose dynamics of children and adolescents with newly diagnosed type 1 diabetes mellitus

Pediatric Diabetes ◽

10.1111/pedi.12374 ◽

2016 ◽

Vol 18 (3) ◽

pp. 196-203 ◽

Cited By ~ 3

Author(s):

Yi Wang ◽

Chunxiu Gong ◽

Bingyan Cao ◽

Xi Meng ◽

Liya Wei ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Type 1 Diabetes Mellitus ◽

Children And Adolescents ◽

Insulin Dosage ◽

Newly Diagnosed ◽

Glucose Dynamics

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A Comparison of Machine Learning Algorithms in Blood Glucose Prediction for People with Type 1 Diabetes

10.1145/3500931.3500993 ◽

2021 ◽

Author(s):

Yiyang Wang

Keyword(s):

Machine Learning ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Glucose Prediction

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Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes

International Journal of Control ◽

10.1080/00207179.2014.897004 ◽

2014 ◽

Vol 87 (7) ◽

pp. 1454-1466 ◽

Cited By ~ 23

Author(s):

Harald Kirchsteiger ◽

Rolf Johansson ◽

Eric Renard ◽

Luigi del Re

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Continuous Time ◽

Model Identification ◽

Time Interval ◽

Interval Model ◽

Glucose Dynamics

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Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions

Sensors ◽

10.3390/s21165273 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5273

Author(s):

Laura Martínez-Delgado ◽

Mario Munoz-Organero ◽

Paula Queipo-Alvarez

Keyword(s):

Machine Learning ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Glucose Level ◽

The Body ◽

World Health ◽

Glucose Levels ◽

Preventive Actions ◽

Health Organization

Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the World Health Organization. Intensive research is being carried out on artificial intelligence methods to help people with diabetes to optimize the way in which they use insulin, carbohydrate intakes, or physical activity. By predicting upcoming levels of blood glucose concentrations, preventive actions can be taken. Previous research studies using machine learning methods for blood glucose level predictions have mainly focused on the machine learning model used. Little attention has been given to the pre-processing of insulin and carbohydrate signals in order to mimic the human absorption processes. In this manuscript, a recurrent neural network (RNN) based model for predicting upcoming blood glucose levels in people with type 1 diabetes is combined with several carbohydrate and insulin absorption curves in order to optimize the prediction results. The proposed method is applied to data from real patients suffering type 1 diabetes mellitus (T1DM). The achieved results are encouraging, obtaining accuracy levels around 0.510 mmol/L (9.2 mg/dl) in the best scenario.

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Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning

Health Informatics Journal ◽

10.1177/1460458219850682 ◽

2019 ◽

Vol 26 (1) ◽

pp. 703-718 ◽

Cited By ~ 4

Author(s):

Josep Vehí ◽

Iván Contreras ◽

Silvia Oviedo ◽

Lyvia Biagi ◽

Arthur Bertachi

Keyword(s):

Machine Learning ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Diabetes Management ◽

Machine Learning Algorithms ◽

Support Vector ◽

Diabetic Patients ◽

Glucose Levels ◽

Prediction And Prevention

Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making.

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Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System (Preprint)

10.2196/preprints.18911 ◽

2020 ◽

Author(s):

Ashenafi Zebene Woldaregay ◽

Ilkka Kalervo Launonen ◽

Eirik Årsand ◽

David Albers ◽

Anna Holubová ◽

...

Keyword(s):

Infectious Disease ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Detection System ◽

Disease Detection ◽

Infection Incidence ◽

Glucose Levels ◽

Blood Glucose Levels ◽

Glucose Dynamics

BACKGROUND Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. OBJECTIVE The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. METHODS We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). RESULTS Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. CONCLUSIONS We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

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Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

Journal of Medical Internet Research ◽

10.2196/18911 ◽

2020 ◽

Vol 22 (8) ◽

pp. e18911

Author(s):

Ashenafi Zebene Woldaregay ◽

Ilkka Kalervo Launonen ◽

Eirik Årsand ◽

David Albers ◽

Anna Holubová ◽

...

Keyword(s):

Infectious Disease ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Detection System ◽

Disease Detection ◽

Infection Incidence ◽

Glucose Levels ◽

Blood Glucose Levels ◽

Glucose Dynamics

Background Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. Objective The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. Methods We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). Results Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. Conclusions We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

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