150 results found
Zhu T, Li K, Herrero P, et 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.
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.
Herrero P, Reddy M, Georgiou P, et al., 2022, Identifying Continuous Glucose Monitoring Data Using Machine Learning, DIABETES TECHNOLOGY & THERAPEUTICS, Vol: 24, Pages: 403-408, ISSN: 1520-9156
Zhu T, Kuang L, Daniels J, et 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.
Daniels J, Herrero P, Georgiou P, 2022, A Multitask Learning Approach to Personalized Blood Glucose Prediction, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 26, Pages: 436-445, ISSN: 2168-2194
Daniels J, Herrero P, Georgiou P, 2022, A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems, SENSORS, Vol: 22
Armiger R, Reddy M, Oliver NS, et al., 2022, An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms., J Diabetes Sci Technol, Vol: 16, Pages: 29-39
BACKGROUND: User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. METHODS: The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. RESULTS: Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). CONCLUSIONS: Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperformi
Hernandez B, Herrero-Viñas P, Rawson TM, et al., 2021, Resistance trend estimation using regression analysis to enhance antimicrobial surveillance: a multi-centre study in London 2009-2016, Antibiotics, Vol: 10, Pages: 1-16, ISSN: 2079-6382
In the last years, there has been an increase of antimicrobial resistance rates around the world with the misuse and overuse of antimicrobials as one of the main leading drivers. In response to this threat, a variety of initiatives have arisen to promote the efficient use of antimicrobials. These initiatives rely on antimicrobial surveillance systems to promote appropriate prescription practices and are provided by national or global health care institutions with limited consideration of the variations within hospitals. As a consequence, physicians’ adherence to these generic guidelines is still limited. To fill this gap, this work presents an automated approach to performing local antimicrobial surveillance from microbiology data. Moreover, in addition to the commonly reported resistance rates, this work estimates secular resistance trends through regression analysis to provide a single value that effectively communicates the resistance trend to a wider audience. The methods considered for trend estimation were ordinary least squares regression, weighted least squares regression with weights inversely proportional to the number of microbiology records available and autoregressive integrated moving average. Among these, weighted least squares regression was found to be the most robust against changes in the granularity of the time series and presented the best performance. To validate the results, three case studies have been thoroughly compared with the existing literature: (i) Escherichia coli in urine cultures; (ii) Escherichia coli in blood cultures; and (iii) Staphylococcus aureus in wound cultures. The benefits of providing local rather than general antimicrobial surveillance data of a higher quality is two fold. Firstly, it has the potential to stimulate engagement among physicians to strengthen their knowledge and awareness on antimicrobial resistance which might encourage prescribers to change their prescription habits more willingly. Moreover, it pro
Rawson TM, Wilson RC, O'Hare D, et al., 2021, Optimizing antimicrobial use: challenges, advances and opportunities, NATURE REVIEWS MICROBIOLOGY, Vol: 19, Pages: 747-758, ISSN: 1740-1526
Rawson TM, Hernandez B, Moore L, et al., 2021, A real-world evaluation of a case-based reasoning algorithm to support antimicrobial prescribing decisions in acute care, Clinical Infectious Diseases, Vol: 72, Pages: 2103-2111, ISSN: 1058-4838
BackgroundA locally developed Case-Based Reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated.MethodsPrescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in two patient populations. Firstly, in patients with confirmed Escherichia coli blood stream infections (‘E.coli patients’), and secondly in ward-based patients presenting with a range of potential infections (‘ward patients’). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the WHO Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known, or most-likely organism antimicrobial sensitivity profile.ResultsIn total, 224 patients (145 E.coli patients and 79 ward patients) were included. Mean (SD) age was 66 (18) years with 108/224 (48%) female gender. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (OR: 1.24 95%CI:0.392-3.936;p=0.71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0-13) compared to 8 (0-12) (p<0.01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians’ prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77 95%CI:1.212-2.588 p<0.01). Results were similar for E.coli and ward patients on subgroup analysis.ConclusionsA CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviours more broadly and patient outcomes.
Leon-Vargas F, Martin C, Garcia-Jaramillo M, et al., 2021, Is a cloud-based platform useful for diabetes management in Colombia? The Tidepool experience, COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol: 208, ISSN: 0169-2607
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
Contreras I, Calm R, Sainz MA, et al., 2021, Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty, MATHEMATICS, Vol: 9
Avari P, Leal Y, Herrero Vinas P, et al., 2021, Safety and feasibility of the PEPPER adaptive bolus advisor and safety system; a randomized control study, Diabetes Technology and Therapeutics, Vol: 23, Pages: 175-186, ISSN: 1520-9156
Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator.Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9–10.0 mmol/L [70–180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender.Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3–49.8) years, diabetes duration 21.0 (11.5–26.0) years, and HbA1c 61.0 (58.0–66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1–67.8] % vs. 58.4 [49.6–64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia.Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confir
Rawson TM, Hernandez B, Wilson R, et al., 2021, Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19, JAC-Antimicrobial Resistance, Vol: 3, Pages: 1-4, ISSN: 2632-1823
Background: Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during COVID-19.Methods: Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted. Demographic, blood test, and microbiology data for individuals with and without SARS-CoV-2 positive PCR were obtained. A Gaussian-Naïve Bayes (GNB), Support Vector Machine (SVM), and Artificial Neuronal Network (ANN) were trained and compared using the area under the receiver operating characteristic curve (AUCROC). The best performing algorithm (SVM with 21 blood test variables) was prospectively piloted in July 2020. AUCROC was calculated for the prediction of a positive microbiological sample within 48 hours of admission. Results: A total of 15,599 daily blood profiles for 1,186 individual patients were identified to train the algorithms. 771/1186 (65%) individuals were SARS-CoV-2 PCR positive. Clinically significant microbiology results were present for 166/1186 (14%) patients during admission. A SVM algorithm trained with 21 routine blood test variables and over 8000 individual profiles had the best performance. AUCROC was 0.913, sensitivity 0.801, and specificity 0.890. Prospective testing on 54 patients on admission (28/54, 52% SARS-CoV-2 PCR positive) demonstrated an AUCROC of 0.960 (0.90-1.00). Conclusion: A SVM using 21 routine blood test variables had excellent performance at inferring the likelihood of positive microbiology. Further prospective evaluation of the algorithms ability to support decision making for the diagnosis of bacterial infection in COVID-19 cohorts is underway.
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
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
Moscardo V, Herrero P, Reddy M, et al., 2020, Assessment of Glucose Control Metrics by Discriminant Ratio, DIABETES TECHNOLOGY & THERAPEUTICS, Vol: 22, Pages: 719-726, ISSN: 1520-9156
Zhu T, Li K, Kuang L, et al., 2020, An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning, SENSORS, Vol: 20
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
Guemes A, Cappon G, Hernandez B, et al., 2020, Predicting quality of overnight glycaemic control in type 1 diabetes using binary classifiers, IEEE Journal of Biomedical and Health Informatics, Vol: 24, Pages: 1439-1446, ISSN: 2168-2194
In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are starting to become a reality, their elevated cost and performance below user expectations is hindering their adoption. Hence, a decision support system that helps people with type 1 diabetes, on multiple daily injections or insulin pump therapy, to avoid undesirable overnight blood glucose fluctuations (hyper- or hypoglycaemic) is an attractive alternative. In this paper, we introduce a novel data-driven approach to predict the quality of overnight glycaemic control in people with type 1 diabetes by analyzing commonly gathered data during the day-time period (continuous glucose monitoring data, meal intake and insulin boluses). The proposed approach is able to predict whether overnight blood glucose concentrations are going to remain within or outside the target range, and therefore allows the user to take the appropriate preventive action (snack or change in basal insulin). For this purpose, a number of popular established machine learning algorithms for classification were evaluated and compared on a publicly available clinical dataset (i.e. OhioT1DM). Although there is no clearly superior classification algorithm, this study indicates that, by using commonly gathered data in type 1 diabetes management, it is possible to predict the quality of overnight glycaemic control with reasonable accuracy (AUC-ROC= 0.7).
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.
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
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
Herrero P, Alalitei A, Reddy M, et al., 2020, Robust determination of the optimal continuous glucose monitoring length of intervention to evaluate long-term glycaemic control, 13th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD 2020), Publisher: Mary Ann Liebert, Pages: A130-A130, ISSN: 1520-9156
Waite M, Aldea A, Avari P, et al., 2020, TRUST AND CONTEXTUAL ENGAGEMENT WITH THE PEPPER SYSTEM: THE QUALITATIVE FINDINGS OF A CLINICAL FEASIBILITY STUDY, Publisher: MARY ANN LIEBERT, INC, Pages: A18-A19, ISSN: 1520-9156
Avari P, Leal Y, Wos M, et al., 2020, Efficacy and safety of the patient empowerment through predictive personalised decision support (pepper) system: an open-label randomised controlled trial, 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: A104-A104, 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
Duce DA, Martin C, Russell A, et al., 2020, Visualizing Usage Data from a Diabetes Management System, Pages: 1-9
This article explores the role for visualization in interpreting data collected by a customised analytics framework within a healthcare technology project. It draws on the work of the EU-funded PEPPER project, which has created a personalised decision-support system for people with type 1 diabetes. Our approach was an exercise in exploratory visualization, as described by Bergeron's three category taxonomy. The charts revealed different patterns of interaction, including variability in insulin dosing schedule, and potential causes of rejected advice. These insights into user behaviour are of especial value to this field, as they may help clinicians and developers understand some of the obstacles that hinder the uptake of diabetes technology.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.