Publications
39 results found
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
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 data sets 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 ten virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control.
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.
King Z, Farrington J, Utley M, et al., 2022, Machine learning for real-time aggregated prediction of hospital admission for emergency patients, NPJ DIGITAL MEDICINE, Vol: 5, ISSN: 2398-6352
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- Citations: 1
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.
King Z, Farrington J, Utley M, et al., 2022, Machine Learning for Real-Time Aggregated Prediction of Hospital Admission for Emergency Patients
<jats:title>Abstract</jats:title><jats:p>Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68-0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.</jats:p>
Shen F, Wang Z, Ding G, et al., 2022, 3D Compressed Spectrum Mapping With Sampling Locations Optimization in Spectrum-Heterogeneous Environment, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 21, Pages: 326-338, ISSN: 1536-1276
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- Citations: 6
Thygesen JH, Tomlinson C, Hollings S, et al., 2021, Understanding COVID-19 trajectories from a nationwide linked electronic health record cohort of 56 million people: phenotypes, severity, waves & vaccination
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Updatable understanding of the onset and progression of individuals COVID-19 trajectories underpins pandemic mitigation efforts. In order to identify and characterize individual trajectories, we defined and validated ten COVID-19 phenotypes from linked electronic health records (EHR) on a nationwide scale using an extensible framework.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Cohort study of 56.6 million people in England alive on 23/01/2020, followed until 31/05/2021, using eight linked national datasets spanning COVID-19 testing, vaccination, primary & secondary care and death registrations data. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity using a combination of international clinical terminologies (e.g. SNOMED-CT, ICD-10) and bespoke data fields; positive test, primary care diagnosis, hospitalisation, critical care (four phenotypes), and death (three phenotypes). Using these phenotypes, we constructed patient trajectories illustrating the transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>We identified 3,469,528 infected individuals (6.1%) with 8,825,738 recorded COVID-19 phenotypes. Of these, 364,260 (11%) were hospitalised and 140,908 (4%) died. Of those hospitalised, 38,072 (10%) were admitted to intensive care (ICU), 54,026 (15%) received non-invasive ventilation and 21,404 (6%) invasive ventilation. Amongst hospitalised patients, first wave mortality (30%) was higher than the second (23%) in non-ICU settings, but remained unchanged for ICU patients. The highest mortality was for patients receiving critical care outside of ICU in wave 1 (51%). 13,0
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: 24
Zhu T, Li K, Herrero P, et 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
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- Citations: 19
Wang Z, Xu X, Qiang X, et al., 2021, Learning Vector Quantization-Aided Detection for MIMO Systems, IEEE COMMUNICATIONS LETTERS, Vol: 25, Pages: 874-878, ISSN: 1089-7798
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: 11
Zhang J, Cong S, Ling Q, et al., 2020, Quantum State Filter With Disturbance and Noise, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 65, Pages: 2856-2866, ISSN: 0018-9286
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- Citations: 6
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
Li K, Daniels J, Liu C, et al., 2020, Convolutional recurrent neural networks for glucose prediction, IEEE Journal of Biomedical and Health Informatics, Vol: 24, Pages: 603-613, ISSN: 2168-2194
Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60-minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79\% for 60-minute). In addition, the model provides competitive performance in providing effective prediction horizon ( PHeff) with minimal time lag both in a simulated patient dataset ( PHeff = 29.0±0.7 for 30-min and PHeff = 49.8±2.9 for 60-min) and in a real patient dataset ( PHeff = 19.3±3.1 for 30-min and PHeff = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.
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.
Zhang J, Cong S, Ling Q, et al., 2019, An Efficient and Fast Quantum State Estimator With Sparse Disturbance, IEEE TRANSACTIONS ON CYBERNETICS, Vol: 49, Pages: 2546-2555, ISSN: 2168-2267
Zhu T, Li K, Georgiou P, 2019, A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning
We propose a dual-hormone delivery strategy by exploiting deep reinforcementlearning (RL) for people with Type 1 Diabetes (T1D). Specifically, doubledilated recurrent neural networks (RNN) are used to learn the hormone deliverystrategy, trained by a variant of Q-learning, whose inputs are raw data ofglucose \& meal carbohydrate and outputs are dual-hormone (insulin andglucagon) delivery. Without prior knowledge of the glucose-insulin metabolism,we run the method on the UVA/Padova simulator. Hundreds days of self-play areperformed to obtain a generalized model, then importance sampling is adopted tocustomize the model for personal use. \emph{In-silico} the proposed strategyachieves glucose time in target range (TIR) $93\%$ for adults and $83\%$ foradolescents given standard bolus, outperforming previous approachessignificantly. The results indicate that deep RL is effective in buildingpersonalized hormone delivery strategy for people with T1D.
Li K, Chandrasekera TC, Li Y, et al., 2018, A Non-Linear Reweighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography, IEEE SENSORS JOURNAL, Vol: 18, Pages: 5049-5057, ISSN: 1530-437X
Yang J, Cong S, Liu X, et al., 2017, Effective quantum state reconstruction using compressed sensing in NMR quantum computing, Physical Review A, Vol: 96, ISSN: 1050-2947
Compressed sensing (CS) has been verified as an effective technique in the reconstruction of quantum state; however, it is still unknown if CS can reconstruct quantum states given the incomplete data measured by nuclear magnetic resonance (NMR). In this paper, we propose an effective NMR quantum state reconstruction method based on CS. Different from the conventional CS-based quantum state reconstruction, our method uses the actual observation data from NMR experiments rather than the data measured by the Pauli operators. We implement measurements on quantum states in practical NMR computing experiments and reconstruct states of two, three, and four qubits using fewer number of measurement settings, respectively. The proposed method is easy to implement and performs more efficiently with the increase of the system dimension size. The performance reveals both efficiency and accuracy, which provides an alternative for the quantum state reconstruction in practical NMR.
Li K, Zheng K, Yang J, et al., 2017, Hybrid reconstruction of quantum density matrix: when low-rank meets sparsity, Quantum Information Processing, Vol: 16, ISSN: 1570-0755
Both the mathematical theory and experiments have verified that the quantumstate tomography based on compressive sensing is an efficient framework for thereconstruction of quantum density states. In recent physical experiments, we foundthat many unknown density matrices in which people are interested in are low-rankas well as sparse. Bearing this information in mind, in this paper we propose a reconstructionalgorithm that combines the low-rank and the sparsity property of densitymatrices and further theoretically prove that the solution of the optimization functioncan be, and only be, the true density matrix satisfying the model with overwhelmingprobability, as long as a necessary number of measurements are allowed. The solverleverages the fixed-point equation technique in which a step-by-step strategy is developedby utilizing an extended soft threshold operator that copes with complex values.Numerical experiments of the density matrix estimation for real nuclear magnetic resonancedevices reveal that the proposed method achieves a better accuracy comparedto some existing methods. We believe that the proposed method could be leveraged asa generalized approach and widely implemented in the quantum state estimation.
Cong S, Wen J, Meng F, et al., 2017, Global Stabilization of Mixed States for Stochastic Quantum Systems via Switching Control, of 20th World Congress of the International Federation of Automatic Control (IFAC), Publisher: Elsevier, Pages: 13032-13037, ISSN: 1474-6670
The global stabilization of mixed states for finite dimensional stochastic quantum systems with non-regular measurement operator and non-diagonal free Hamiltonian is investigated in this paper. A two-part switching control strategy is proposed, in which the constant control is used to steer the system state to enter the convergence domain, while the control law which is designed based on Lyapunov stability theorem is used to attract the system state in convergence domain to the target state. The convergence of the switching control is strictly proved. Moreover, the numerical experiments on a three dimensional stochastic quantum system are implemented to demonstrate the effectiveness of the control proposed.
Zhang J, Li K, Cong S, et al., 2017, Efficient reconstruction of density matrices for high dimensional quantum state tomography, Signal Processing, Vol: 139, Pages: 136-142, ISSN: 0165-1684
Li K, Zhang J, Cong S, 2017, Fast reconstruction of high-qubit-number quantum states via low-rate measurements, PHYSICAL REVIEW A, Vol: 96, ISSN: 2469-9926
Zheng K, Li K, Cong S, 2016, A reconstruction algorithm for compressive quantum tomography using various measurement sets, SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322
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- Citations: 14
Harraz S, Yang J, Li K, et al., 2016, Quantum state transfer control based on the optimal measurement, Optimal Control Applications and Methods, Vol: 38, Pages: 744-753, ISSN: 0143-2087
This paper explores the optimal control of quantum state transfer in a two-dimensional quantum system by a sequence of non-selective projection measurements. We show that for a given initial state, one can always find the corresponding projection operator that can effectively drive the given initial state to any arbitrary target pure state. An external control field is proposed to compensate the effect of the free evolution of system. Numerical simulations and characteristics analysis are given in three cases: without considering free evolution, considering free evolution, and with the action of external control field. The simulating experimental results show that the optimal measurement control is more effective by using proposed external control field.
Ma R, Liu E, Wang R, et al., 2016, Energy-Aware Preferential Attachment Model for Wireless Sensor Networks with Improved Survivability, KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, Vol: 10, Pages: 3066-3079, ISSN: 1976-7277
Li K, Zhang H, Kuang S, et al., 2016, An improved robust ADMM algorithm for quantum state tomography, QUANTUM INFORMATION PROCESSING, Vol: 15, Pages: 2343-2358, ISSN: 1570-0755
Li K, Rojas CR, Yang T, et al., 2016, Piecewise Sparse Signal Recovery Via Piecewise Orthogonal Matching Pursuit, IEEE 41th International Conference on Acoustics, Speech and Signal Processing, (ICASSP 2016), Publisher: IEEE, ISSN: 2379-190X
In this paper, we consider the recovery of piecewise sparse signals from incomplete noisy measurements via a greedy algorithm. Here piecewise sparse means that the signal can be approximated in certain domain with known number of nonzero entries in each piece/segment. This paper makes a two-fold contribution to this problem: 1) formulating a piecewise sparse model in the framework of compressed sensing and providing the theoretical analysis of corresponding sensing matrices; 2) developing a greedy algorithm called piecewise orthogonal matching pursuit (POMP) for the recovery of piecewise sparse signals. Experimental simulations verify the effectiveness of the proposed algorithms.
Li K, Sundin M, Rojas CR, et al., 2015, Alternating strategies with internal ADMM for low-rank matrix reconstruction, Signal Processing, Vol: 121, Pages: 153-159, ISSN: 0165-1684
This paper focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover low-rank matrices with linear parameterized structures, such as Hankel matrices. The use of ADMM helps to improve the estimate in each iteration due to its capability of incorporating information about the direction of estimates achieved in previous iterations. We show that merging these two alternating strategies leads to a better performance and less consumed time than the existing alternating least squares (ALS) strategy. The improved performance is verified via numerical simulations with varying sampling rates and real applications.
Li K, Cong S, 2015, State of the art and prospects of structured sensing matrices in compressed sensing, Frontiers of Computer Science, Vol: 9, Pages: 665-677, ISSN: 2095-2228
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