Leveraging a Big Dataset to Develop a Recurrent Neural Network to Predict Adverse Glycemic Events in Type 1 Diabetes

Mosquera-Lopez, Clara; Dodier, Robert H.; Tyler, Nichole S.; Resalat, Navid; Jacobs, Peter G.

doi:10.1109/jbhi.2019.2911701

Cited by 36 publications

(22 citation statements)

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“…Zhu et al in [ 28 ] used CGM data together with the insulin values and carbohydrate intake estimations in the dataset in [ 21 ] and a deep learning model to achieve an RMSE value of 21.7 mg/dL on a 30 min prediction horizon. The research study in [ 29 ] used both CGM and insulin data and a deep learning model based on RNN with LSTM cells to predict the levels of BG in the next 30 min. The study achieved an RMSE value of 7.55 mg/dL and anticipated the occurrence of 97.79% of hyperglycemia events (glucose > 180 mg/dL), and 90.87% of hypoglycemia events (glucose < 70 mg/dL).…”

Section: Related Workmentioning

confidence: 99%

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

Muñoz-Organero

2020

Sensors

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Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the base for control algorithms in glucose regulating systems such as the artificial pancreas. Numerous research studies have already been conducted in order to provide predictions for blood glucose levels with particularities in the input signals and underlying models used. These models can be categorized into two major families: those based on tuning glucose physiological-metabolic models and those based on learning glucose evolution patterns based on machine learning techniques. This paper reviews the state of the art in blood glucose predictions for T1DM patients and proposes, implements, validates and compares a new hybrid model that decomposes a deep machine learning model in order to mimic the metabolic behavior of physiological blood glucose methods. The differential equations for carbohydrate and insulin absorption in physiological models are modeled using a Recurrent Neural Network (RNN) implemented using Long Short-Term Memory (LSTM) cells. The results show Root Mean Square Error (RMSE) values under 5 mg/dL for simulated patients and under 10 mg/dL for real patients.

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Section: Related Workmentioning

confidence: 99%

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

Muñoz-Organero

2020

Sensors

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“…These systems showed moderate performance in terms of sensitivity and specificity. A recurrent neural network (RNN) was trained by Mosquera et al [ 81 ] using BG and insulin data for adverse glycemic event prediction. This system was reported to be more than 90% accurate in predicting hypoglycemic events.…”

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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“…That model had better prediction results than the average linear model and daily model predictor (DMP) [25]. Mosquera-Lopez et al [26] developed a deep learning network that achieved superior prediction performance with the aid of huge amounts of blood glucose data (27,466 days). This model produced more accurate predictions than other machine learning approaches.…”

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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Leveraging a Big Dataset to Develop a Recurrent Neural Network to Predict Adverse Glycemic Events in Type 1 Diabetes

Cited by 36 publications

References 0 publications

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

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

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