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

Vehı́, Josep; Contreras, Iván; Oviedo, Silvia; Biagi, Lyvia; Bertachi, Arthur

doi:10.1177/1460458219850682

Cited by 52 publications

(53 citation statements)

References 44 publications

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“…Aiello et al [ 67 ] and Oviedo et al [ 53 ] both aimed at postprandial hypoglycemia prediction by utilizing BG data combined with insulin and CHO data. Noaro et al [ 72 ] proposed an insulin bolus calculator while Vehi et al [ 59 ] proposed a hypoglycemia prediction and prevention system that employed BG, insulin, and CHO data for ML model training. A DSS that provides weekly insulin dosage recommendations for type1 diabetics was proposed by Tyler et al [ 61 ].…”

Section: Resultsmentioning

confidence: 99%

“…These studies have employed multiple variants of ANN such as RNN, DL, CNN, MLPs, etc. ANNs were used by Bertachi et al [ 41 ], Vahedi et al [ 33 ], Zhu et al [ 64 ], Mosquera-Lopez et al [ 81 ], San et al [ 35 ], Jin et al [ 36 ], Mhaskar et al [ 63 ], Li et al [ 74 ], Li et al [ 78 ], Bertachi et al [ 51 ], Güemes et al [ 60 ], Oviedo et al [ 53 ], Vehi et al [ 59 ], Quan et al [ 50 ], and Amar et al [ 75 ]. Unlike other ML models, ANNs extract their own features from the inputs based on their hidden parameters.…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Mujahid

Contreras

Vehı́

2021

Sensors

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(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Mujahid

Contreras

Vehı́

2021

Sensors

Self Cite

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show abstract

“…Last, Vehì et al [ 61 ] proposed an AI-based DSS, called the Patient Safety System, for the prediction and prevention of hypoglycemic events and the classification of glycemic control profiles. The system is shown in Figure 5 and incorporates 4 modules: A grammatical evolution algorithm for 60-min ahead, personalized, glucose concentration level prediction, that uses, as input features, the CGM data, the IOB and the carbohydrates rate-of-appearance over the past 2 h [ 62 ].…”

Section: Glucose Predictionmentioning

confidence: 99%

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

Vettoretti

Cappon

Facchinetti

et al. 2020

Sensors

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Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1–5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient’s data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.

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“…We expect that with further technology advancement as with the utilization of machine learning and artificial intelligence capabilities to predict and prevent hypoglycaemic events may minimize hypoglycaemia harmful effects. Those tools will surely assume an important place in the world of hypoglycaemia assessment and management 107, 108 . Technology can be included as an important tool for evaluation of hypoglycaemia episodes, the identification of people at risk for hypoglycaemia and for prevention of hypoglycaemia events without compromising glycaemic control.…”

Section: Technology and Hypoglycaemiamentioning

confidence: 99%

Hypoglycaemia and its management in primary care setting

Ibrahim¹,

Baker

Cahn³

et al. 2020

Diabetes Metabolism Res

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Hypoglycaemia is common in patients with type 1 diabetes and type 2 diabetes and constitutes a major limiting factor in achieving glycaemic control among people with diabetes. While hypoglycaemia is defined as a blood glucose level under 70 mg/dL (3.9 mmol/L), symptoms may occur at higher blood glucose levels in individuals with poor glycaemic control. Severe hypoglycaemia is defined as an episode requiring the assistance of another person to actively administer carbohydrate, glucagon, or take other corrective actions to assure neurologic recovery. Hypoglycaemia is the most important safety outcome in clinical studies of glucose lowering agents. The American Diabetes Association Standards of Medical Care recommends that a management protocol for hypoglycaemia should be designed and implemented by every hospital, along with a clear prevention and treatment plan. A tailored approach, using clinical and pathophysiologic disease stratification, can help individualize glycaemic goals and promote new therapies to improve quality of life of patients. Data from recent large clinical trials reported low risk of hypoglycaemic events with the use of newer anti‐diabetic drugs. Increased hypoglycaemia risk is observed with the use of insulin and/or sulphonylureas. Vulnerable patients with T2D at dual risk of severe hypoglycaemia and cardiovascular outcomes show features of “frailty.” Many of such patients may be better treated by the use of GLP‐1 receptor agonists or SGLT2 inhibitors rather than insulin. Continuous glucose monitoring (CGM) should be considered for all individuals with increased risk for hypoglycaemia, impaired hypoglycaemia awareness, frequent nocturnal hypoglycaemia and with history of severe hypoglycaemia. Patients with impaired awareness of hypoglycaemia benefit from real‐time CGM. The diabetes educator is an invaluable resource and can devote the time needed to thoroughly educate the individual to reduce the risk of hypoglycaemia and integrate the information within the entire construct of diabetes self‐management. Conversations about hypoglycaemia facilitated by a healthcare professional may reduce the burden and fear of hypoglycaemia among patients with diabetes and their family members. Optimizing insulin doses and carbohydrate intake, in addition to a short warm up before or after the physical activity sessions may help avoiding hypoglycaemia. Several therapeutic considerations are important to reduce hypoglycaemia risk during pregnancy including administration of rapid‐acting insulin analogues rather than human insulin, pre‐conception initiation of insulin analogues, and immediate postpartum insulin dose reduction.

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

Cited by 52 publications

References 44 publications

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

Hypoglycaemia and its management in primary care setting

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