Imperial College London

MrTaiyuZhu

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Postgraduate
 
 
 
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Contact

 

taiyu.zhu17

 
 
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Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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21 results found

Zhu T, Li K, Herrero P, Georgiou Pet al., 2022, Personalized blood glucose prediction for Type 1 diabetes using evidential deep learning and meta-learning., IEEE Transactions on Biomedical Engineering, 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.

Journal article

Zhu T, Uduku C, Li K, Herrero Vinas P, Oliver N, Georgiou Pet 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.

Journal article

Zhu T, Li K, Herrero P, Georgiou Pet al., 2022, RECURRENT GENERATIVE ADVERSARIAL NETWORKS FOR GLUCOSE TIME SERIES GENERATION, Publisher: MARY ANN LIEBERT, INC, Pages: A229-A229, ISSN: 1520-9156

Conference paper

Zhu T, Kuang L, Daniels J, Herrero P, Li K, Georgiou Pet al., 2022, IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing, IEEE Internet of Things Journal

Blood glucose (BG) prediction is essential to the success of glycemic control in type 1 diabetes (T1D) management. Empowered by the recent development of the Internet of Medical Things (IoMT), continuous glucose monitoring (CGM) and deep learning technologies have been demonstrated to achieve the state of the art in BG prediction. However, it is challenging to implement such algorithms in actual clinical settings to provide persistent decision support due to the high demand for computational resources, while smartphone-based implementations are limited by short battery life and require users to carry the device. In this work, we propose a new deep learning model using an attention-based evidential recurrent neural network and design an IoMT-enabled wearable device to implement the embedded model, which comprises a low-cost and low-power system on a chip to perform Bluetooth connectivity and edge computing for real-time BG prediction and predictive hypoglycemia detection. In addition, we developed a smartphone app to visualize BG trajectories and predictions, and desktop and cloud platforms to backup data and fine-tune models. The embedded model was evaluated on three clinical datasets including 47 T1D subjects. The proposed model achieved superior performance of root mean square error (RMSE), mean absolute error, and glucose-specific RMSE, and obtained the best accuracy for hypoglycemia detection when compared with a group of machine learning baseline methods. Moreover, we performed hardware-in-the-loop in silico trials with 10 virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control.

Journal article

Zhu T, Li K, Herrero P, Georgiou Pet al., 2021, Deep Learning for Diabetes: A Systematic Review, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 2744-2757, ISSN: 2168-2194

Journal article

Uduku C, Zhu T, Li K, Daniels J, Herrero P, Oliver N, Georgiou P, Reddy Met 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

Conference paper

Zhu T, Li K, Herrero P, Georgiou Pet 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

Conference paper

Zhu T, Li K, Herrero P, Georgiou Pet 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

Conference paper

Zhu T, Li K, Herrero P, Georgiou Pet al., 2021, Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 1223-1232, ISSN: 2168-2194

Journal article

Zhu T, Kuang L, Li K, Zeng J, Herrero P, Georgiou Pet 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

Conference paper

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.

Conference paper

Kuang L, Zhu T, Li K, Daniels J, Herrero P, Georgiou Pet 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

Conference paper

Zhu T, Li K, Kuang L, Herrero P, Georgiou Pet al., 2020, An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning, SENSORS, Vol: 20

Journal article

Zhu T, Li K, Chen J, Herrero P, Georgiou Pet 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

Journal article

Daniels J, Zhu T, Li K, Uduku C, Herrero P, Oliver N, Georgiou Pet 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

Conference paper

Spence R, Li K, Uduku C, Zhu T, Redmond L, Herrero P, Oliver N, Georgiou Pet 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

Conference paper

Zhu T, Li K, Uduku C, Herrero P, Oliver N, Georgiou Pet 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

Conference paper

Zhu T, Yao X, Li K, Herrero P, Georgiou Pet 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.

Conference paper

Li K, Liu C, Zhu T, Herrero P, Georgiou Pet 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.

Journal article

Zhu T, Li K, Herrero P, Chen J, Georgiou Pet 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.

Conference paper

Chen J, Li K, Herrero P, Zhu T, Georgiou Pet 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).

Conference paper

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