Publications
27 results found
Zhu T, Kuang L, Piao C, et al., 2024, Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge., IEEE Trans Biomed Circuits Syst, Vol: 18, Pages: 236-246
Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.
Zhu T, Li K, Georgiou P, 2023, Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes., IEEE J Biomed Health Inform, Vol: 27, Pages: 5087-5098
Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas (AP), have been shown to optimize glucose control and reduce the self-management burdens for people living with type 1 diabetes (T1D). AP systems can adjust the basal infusion rates of insulin pumps, facilitated by real-time communication with continuous glucose monitoring. Deep reinforcement learning (DRL) has introduced new paradigms of basal insulin control algorithms. However, all the existing DRL-based AP controllers require extensive random online interactions between the agent and environment. While this can be validated in T1D simulators, it becomes impractical in real-world clinical settings. To this end, we propose an offline DRL framework that can develop and validate models for basal insulin control entirely offline. It comprises a DRL model based on the twin delayed deep deterministic policy gradient and behavior cloning, as well as off-policy evaluation (OPE) using fitted Q evaluation. We evaluated the proposed framework on an in silico dataset generated by the UVA/Padova T1D simulator, and the OhioT1DM dataset, a real clinical dataset. The performance on the in silico dataset shows that the offline DRL algorithm significantly increased time in range while reducing time below range and time above range for both adult and adolescent groups. Then, we used the OPE to estimate model performance on the clinical dataset, where a notable increase in policy values was observed for each subject. The results demonstrate that the proposed framework is a viable and safe method for improving personalized basal insulin control in T1D.
Zhu T, Li K, Herrero P, et al., 2023, GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks., IEEE J Biomed Health Inform, Vol: 27, Pages: 5122-5133
Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials.
Noaro G, Zhu T, Cappon G, et al., 2023, A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q- Learning to Improve Type 1 Diabetes Management, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 27, Pages: 2536-2544, ISSN: 2168-2194
Zhu T, Kuang L, Daniels J, et al., 2023, IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing, IEEE INTERNET OF THINGS JOURNAL, Vol: 10, Pages: 3706-3719, ISSN: 2327-4662
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- Citations: 10
Zhu T, Li K, Herrero P, et al., 2023, Personalized blood glucose prediction for Type 1 diabetes using evidential deep learning and meta-learning., IEEE Transactions on Biomedical Engineering, Vol: 70, Pages: 193-204, ISSN: 0018-9294
The availability of large amounts of data from continuous glucose monitoring (CGM), together with the latest advances in deep learning techniques, have opened the door to a new paradigm of algorithm design for personalized blood glucose (BG) prediction in type 1 diabetes (T1D) with superior performance. However, there are several challenges that prevent the widespread implementation of deep learning algorithms in actual clinical settings, including unclear prediction confidence and limited training data for new T1D subjects. To this end, we propose a novel deep learning framework, Fast-adaptive and Confident Neural Network (FCNN), to meet these clinical challenges. In particular, an attention-based recurrent neural network is used to learn representations from CGM input and forward a weighted sum of hidden states to an evidential output layer, aiming to compute personalized BG predictions with theoretically supported model confidence. The model-agnostic meta-learning is employed to enable fast adaptation for a new T1D subject with limited training data. The proposed framework has been validated on three clinical datasets. In particular, for a dataset including 12 subjects with T1D, FCNN achieved a root mean square error of 18.64±2.60 mg/dL and 31.07±3.62 mg/dL for 30 and 60-minute prediction horizons, respectively, which outperformed all the considered baseline methods with significant improvements. These results indicate that FCNN is a viable and effective approach for predicting BG levels in T1D. The well-trained models can be implemented in smartphone apps to improve glycemic control by enabling proactive actions through real-time glucose alerts.
Xu Y, Kuang L, Zhu T, et al., 2023, Drift Prediction and Chemical Reaction Identification for ISFETs using Deep Learning, 56th IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Zhu T, Chen T, Kuang L, et al., 2023, Edge-Based Temporal Fusion Transformer for Multi-Horizon Blood Glucose Prediction, 56th IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Zhu T, Uduku C, Li K, et al., 2022, Enhancing self-management in type 1 diabetes with wearables and deep learning, npj Digital Medicine, Vol: 5, ISSN: 2398-6352
People living with type 1 diabetes (T1D) require lifelong selfmanagement to maintain glucose levels in a safe range. Failure to do socan lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1Dself-management for real-time glucose measurements, while smartphoneapps are adopted as basic electronic diaries, data visualization tools, andsimple decision support tools for insulin dosing. Applying a mixed effectslogistic regression analysis to the outcomes of a six-week longitudinalstudy in 12 T1D adults using CGM and a clinically validated wearablesensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- andhyperglycemic events measured an hour later. We proceeded to developa new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of mealand bolus insulin, and the sensor wristband to predict glucose levels andhypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE)of 35.28±5.77 mg/dL with the Matthews correlation coefficients fordetecting hypoglycemia and hyperglycemia of 0.56±0.07 and 0.70±0.05,respectively. The use of wristband data significantly reduced the RMSEby 2.25 mg/dL (p < 0.01). The well-trained model is implemented onthe ARISES app to provide real-time decision support. These resultsindicate that the ARISES has great potential to mitigate the risk ofsevere complications and enhance self-management for people with T1D.
Zhu T, Li K, Herrero P, et al., 2022, RECURRENT GENERATIVE ADVERSARIAL NETWORKS FOR GLUCOSE TIME SERIES GENERATION, Publisher: MARY ANN LIEBERT, INC, Pages: A229-A229, ISSN: 1520-9156
Zhu T, Li K, Herrero P, et al., 2021, Deep Learning for Diabetes: A Systematic Review, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 2744-2757, ISSN: 2168-2194
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- Citations: 53
Zhu T, Li K, Herrero P, et al., 2021, BLOOD GLUCOSE PREDICTION FOR TYPE 1 DIABETES WITH POPULATION DATA AND MODEL-AGNOSTIC META-LEARNING, Publisher: MARY ANN LIEBERT, INC, Pages: A100-A100, ISSN: 1520-9156
Uduku C, Zhu T, Li K, et al., 2021, INDEPENDENT PREDICTORS OF HYPOGLYCAEMIA AND IMPENDING HYPOGLYCAEMIA USING A WEARABLE PHYSIOLOGICAL DATA ACQUISITION SENSOR, Publisher: MARY ANN LIEBERT, INC, Pages: A56-A57, ISSN: 1520-9156
Zhu T, Li K, Herrero P, et al., 2021, PERSONALIZED BLOOD GLUCOSE PREDICTION FOR TYPE 1 DIABETES WITH DEEP NEURAL NETWORKS AND ATTENTION MECHANISM, Publisher: MARY ANN LIEBERT, INC, Pages: A100-A101, ISSN: 1520-9156
Zhu T, Li K, Herrero P, et al., 2021, Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An <i>In Silico</i> Validation, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 1223-1232, ISSN: 2168-2194
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- Citations: 33
Zhu T, Li K, Georgiou P, 2021, Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning, Pages: 45-53, ISSN: 1860-949X
We introduce a dual-hormone control algorithm for people with Type 1 Diabetes (T1D) which uses deep reinforcement learning (RL). Specifically, double dilated recurrent neural networks are used to learn the control strategy, trained by a variant of Q-learning. The inputs to the model include the real-time sensed glucose and meal carbohydrate content, and the outputs are the actions necessary to deliver dual-hormone (basal insulin and glucagon) control. Without prior knowledge of the glucose-insulin metabolism, we develop a data-driven model using the UVA/Padova Simulator. We first pre-train a generalized model using long-term exploration in an environment with average T1D subject parameters provided by the simulator, then adopt importance sampling to train personalized models for each individual. In-silico, the proposed algorithm largely reduces adverse glycemic events, and achieves time in range, i.e., the percentage of normoglycemia, for the adults and for the adolescents, which outperforms previous approaches significantly. These results indicate that deep RL has great potential to improve the treatment of chronic diseases such as diabetes.
Zhu T, Kuang L, Li K, et al., 2021, Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
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- Citations: 1
Kuang L, Zhu T, Li K, et al., 2021, Live Demonstration: An IoT Wearable Device for Real-time Blood Glucose Prediction with Edge AI, IEEE Biomedical Circuits and Systems Conference (IEEE BioCAS), Publisher: IEEE
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- Citations: 1
Zhu T, Li K, Chen J, et al., 2020, Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes, JOURNAL OF HEALTHCARE INFORMATICS RESEARCH, Vol: 4, Pages: 308-324, ISSN: 2509-4971
Zhu T, Li K, Kuang L, et al., 2020, An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning, SENSORS, Vol: 20
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- Citations: 17
Zhu T, Li K, Uduku C, et al., 2020, PERSONALIZED MEAL INSULIN BOLUS FOR TYPE 1 DIABETES USING DEEP REINFORCEMENT LEARNING, Publisher: MARY ANN LIEBERT, INC, Pages: A115-A116, ISSN: 1520-9156
Spence R, Li K, Uduku C, et al., 2020, A NOVEL HAND-HELD INTERFACE SUPPORTING THE SELF-MANAGEMENT OF TYPE 1 DIABETES, Publisher: MARY ANN LIEBERT, INC, Pages: A58-A58, ISSN: 1520-9156
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- Citations: 1
Daniels J, Zhu T, Li K, et al., 2020, Arises: an advanced clinical decision support platform for the management of type 1 diabetes, The conference name (including place and date(s) of the conference): 13th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD 2020), Publisher: Mary Ann Liebert, Pages: A57-A57, ISSN: 1520-9156
Zhu T, Yao X, Li K, et al., 2020, Blood glucose prediction for type 1 diabetes using generative adversarial networks, Pages: 90-94, ISSN: 1613-0073
Maintaining blood glucose in a target range is essential for people living with Type 1 diabetes in order to avoid excessive periods in hypoglycemia and hyperglycemia which can result in severe complications. Accurate blood glucose prediction can reduce this risk and enhance early interventions to improve diabetes management. However, due to the complex nature of glucose metabolism and the various lifestyle related factors which can disrupt this, diabetes management still remains challenging. In this work we propose a novel deep learning model to predict future BG levels based on the historical continuous glucose monitoring measurements, meal ingestion, and insulin delivery. We adopt a modified architecture of the generative adversarial network that comprises of a generator and a discriminator. The generator computes the BG predictions by a recurrent neural network with gated recurrent units, and the auxiliary discriminator employs a one-dimensional convolutional neural network to distinguish between the predictive and real BG values. Two modules are trained in an adversarial process with a combination of loss. The experiments were conducted using the OhioT1DM dataset that contains the data of six T1D contributors over 40 days. The proposed algorithm achieves an average root mean square error (RMSE) of 18.34 ± 0.17 mg/dL with a mean absolute error (MAE) of 13.37 ± 0.18 mg/dL for the 30-minute prediction horizon (PH) and an average RMSE of 32.31 ± 0.46 mg/dL with a MAE of 24.20 ± 0.42 for the 60-minute PH. The results are compared for clinical relevance using the Clarke error grid which confirms the promising performance of the proposed model.
Li K, Liu C, Zhu T, et al., 2019, GluNet: A deep learning framework for accurate glucose forecasting., IEEE Journal of Biomedical and Health Informatics, Vol: 24, Pages: 414-423, ISSN: 2168-2194
For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in-silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28 ± 2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83 ± 3.49 mg/dL) with time lag (17.78 ± 8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm.
Zhu T, Li K, Herrero P, et al., 2018, A deep learning algorithm for personalized blood glucose prediction, 3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018, Pages: 64-78, ISSN: 1613-0073
A convolutional neural network (CNN) model is presented to forecast the future glucose levels of the patients with type 1 diabetes. The model is a modified version of a recently proposed model called WaveNet, which becomes very useful in acoustic signal processing. By transferring the task into a classification problem, the model is mainly built by casual dilated CNN layers and employs fast WaveNet algorithms. The OhioT1DM dataset is the source of the four input fields: glucose levels, insulin events, carbohydrate intake and time index. The data is fed into the network along with the targets of the glucose change in 30 minutes. Several pre-processing approaches such as interpolation, combination and filtering are used to fill up the missing data in the training sets, and they improve the performance. Finally, we obtain the predictions of the testing dataset and evaluate the results by the root mean squared error (RMSE). The mean value of the best RMSE of six patients is 21.72.
Chen J, Li K, Herrero P, et al., 2018, Dilated recurrent neural network for short-time prediction of glucose concentration, 3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Pages: 69-73, ISSN: 1613-0073
Diabetes is one of the diseases affecting 415 million people in the world. Developing a robust blood glucose (BG) prediction model has a profound influence especially important for the diabetes management. Subjects with diabetes need to adjust insulin doses according to the blood glucose levels to maintain blood glucose in a target range. An accurate glucose level prediction is able to provide subjects with diabetes with the future glucose levels, so that proper actions could be taken to avoid short-term dangerous consequences or long-term complications. With the developing of continuous glucose monitoring (CGM) systems, the accuracy of predicting the glucose levels can be improved using the machine learning techniques. In this paper, a new deep learning technique, which is based on the Dilated Recurrent Neural Network (DRNN) model, is proposed to predict the future glucose levels for prediction horizon (PH) of 30 minutes. And the method also can be implemented in real-time prediction as well. The result reveals that using the dilated connection in the RNN network, it can improve the accuracy of short-time glucose predictions significantly (RMSE = 19.04 in the blood glucose level prediction (BGLP) on and only on all data points provided).
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