An ARIMA Model With Adaptive Orders for Predicting Blood Glucose Concentrations and Hypoglycemia

Yang, Jun; Li, Lei; Shi, Yan; Xie, Xiaolei

doi:10.1109/jbhi.2018.2840690

Cited by 96 publications

(62 citation statements)

References 36 publications

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“…However, the models needed to tune their parameters at every sampling and the predicted glucose fluctuated significantly as the forgetting factor got smaller. An autoregressive integrated moving-average (ARIMA) model with an adaptive algorithm was recently proposed [18]. When the proposed model predicted hypoglycemia with the 30-min prediction horizon, time of detection was earlier and false alarm rate was lower than other models such as an adaptive univariate model and a general ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

A machine-learning approach to predict postprandial hypoglycemia

Seo

Lee

et al. 2019

BMC Med Inform Decis Mak

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BackgroundFor 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.MethodsWe 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.ResultsIn 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.ConclusionIn 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.

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Section: Introductionmentioning

confidence: 99%

A machine-learning approach to predict postprandial hypoglycemia

Seo

Lee

et al. 2019

BMC Med Inform Decis Mak

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“…Several algorithms for the real-time prediction of future glucose levels have recently been developed, these can be grouped according to different criteria. A first categorization can be made according to the algorithm inputs: some algorithms use CGM data only [ 51 ]. While, others use CGM data plus external inputs, such as the amount of ingested carbohydrates, injected insulin and physical activity [ 52 ].…”

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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“…e ARIMA is a commonly used time-series model which achieves the object's characteristics of self-similarity, periodicity, suddenness, and trends [23] and has a better achievement in the short-term subject's forecast. erefore, it has been applied in the prediction of the stock price index, the blood glucose concentrations [24], the current blockchain technology [25], the wind generation [26], and so on. e BP neural network is a commonly used timeseries and nonlinear prediction model applied in prediction of short-term wind power, indoor temperature, wind speed [27], and hydraulic press machine.…”

Section: Models and Territoriesmentioning

confidence: 99%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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An ARIMA Model With Adaptive Orders for Predicting Blood Glucose Concentrations and Hypoglycemia

Cited by 96 publications

References 36 publications

A machine-learning approach to predict postprandial hypoglycemia

A machine-learning approach to predict postprandial hypoglycemia

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

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