Imperial College London

Dr Pau Herrero

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Visiting Researcher
 
 
 
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Contact

 

p.herrero-vinias

 
 
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Location

 

B422Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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143 results found

Daniels J, Herrero P, Georgiou P, 2022, A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems, Sensors, Vol: 22, Pages: 466-466

<jats:p>Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, p&lt; 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p&lt; 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.</jats:p>

Journal article

Armiger R, Reddy M, Oliver NS, Georgiou P, Herrero Pet 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

Journal article

Hernandez B, Herrero-Viñas P, Rawson TM, Moore LSP, Holmes A, Georgiou Pet 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

Journal article

Leon-Vargas F, Martin C, Garcia-Jaramillo M, Aldea A, Leal Y, Herrero P, Reyes A, Henao D, Maria Gomez Aet 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

Journal article

Daniels J, Herrero P, Georgiou P, 2021, A Multitask Learning Approach to Personalised Blood Glucose Prediction., IEEE J Biomed Health Inform, Vol: PP

Blood glucose prediction algorithms are key tools in the development of decision support systems and closed-loop insulin delivery systems for blood glucose control in diabetes. Deep learning models have provided leading results among machine learning algorithms to date in glucose prediction. However these models typically require large amounts of data to obtain best personalised glucose prediction results. Multitask learning facilitates an approach for leveraging data from multiple subjects while still learning accurate personalised models. In this work we present results comparing the effectiveness of multitask learning over sequential transfer learning, and learning only on subject-specific data with neural networks and support vector regression. The multitask learning approach shows consistent leading performance in predictive metrics at both short-term and long-term prediction horizons. We obtain a predictive accuracy (RMSE) of 18.8 2.3, 25.3 2.9, 31.8 3.9, 41.2 4.5, 47.2 4.6 mg/dL at 30, 45, 60, 90, and 120 min prediction horizons respectively, with at least 93\% clinically acceptable predictions using the Clarke Error Grid (EGA) at each prediction horizon. We also identify relevant prior information such as glycaemic variability that can be incorporated to improve predictive performance at long-term prediction horizons. Furthermore, we demonstrate consistent performance - 5% change in both RMSE and EGA (Zone A) - in rare cases of adverse glycaemic events with 1-6 weeks of training data. In conclusion, a multitask approach can allow for deploying personalised models even with significantly less subject-specific data without compromising performance.

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

Rawson TM, Wilson RC, O'Hare D, Herrero P, Kambugu A, Lamorde M, Ellington M, Georgiou P, Cass A, Hope WW, Holmes AHet al., 2021, Optimizing antimicrobial use: challenges, advances and opportunities, NATURE REVIEWS MICROBIOLOGY, Vol: 19, Pages: 747-758, ISSN: 1740-1526

Journal article

Rawson TM, Hernandez B, Moore L, Herrero P, Charani E, Ming D, Wilson R, Blandy O, Sriskandan S, Toumazou C, Georgiou P, Holmes Aet 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.

Journal article

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

Avari P, Leal Y, Herrero Vinas P, Wos M, Jugnee N, Arnoriaga-Rodríguez M, Thomas M, Liu C, Massana Q, Lopez B, Nita L, Martin C, Fernández-Real JM, Oliver N, Fernández-Balsells M, Reddy Met 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

Journal article

Contreras I, Calm R, Sainz MA, Herrero P, Vehi Jet al., 2021, Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty, MATHEMATICS, Vol: 9

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

Moscardo V, Herrero P, Reddy M, Hill NR, Georgiou P, Oliver Net al., 2020, Assessment of Glucose Control Metrics by Discriminant Ratio, DIABETES TECHNOLOGY & THERAPEUTICS, Vol: 22, Pages: 719-726, ISSN: 1520-9156

Journal article

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

Guemes A, Cappon G, Hernandez B, Reddy M, Oliver N, Georgiou P, Herrero Pet 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).

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

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

Waite M, Aldea A, Avari P, Leal Y, Martin C, Fernandez-Balsells M, Fernandez-Real J, Herrero P, Jugnee N, Lopez B, Reddy M, Wos M, Oliver Net 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

Conference paper

Avari P, Leal Y, Wos M, Jugnee N, Thomas M, Massana J, Lopez B, Nita L, Martin C, Herrero P, Oliver N, Fernandez-Real J, Reddy M, Fernandez-Balsells Met 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

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

Li K, Daniels J, Liu C, Herrero-Vinas P, Georgiou Pet 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.

Journal article

Herrero P, Alalitei A, Reddy M, Georgiou P, Oliver Net 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

Conference paper

Liu C, Avari P, Leal Y, Wos M, Sivasithamparam K, Georgiou P, Reddy M, Fernández-Real JM, Martin C, Fernández-Balsells M, Oliver N, Herrero Pet al., 2020, A modular safety system for an insulin dose recommender: a feasibility study., Journal of Diabetes Science and Technology, Vol: 14, Pages: 87-96, ISSN: 1932-2968

BACKGROUND: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. METHODS: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. RESULTS: Overall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint ( P = .02). This was associated with a significant increase in percentage time in target range (3.9-10.0 mmol/l) from 52.8% (38.3-61.5) to 61.3% (47.5-71.7) ( P = .03). There was also a reduction in number of carbohydrate recommendations. CONCLUSION: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

Journal article

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

Daniels J, Herrero P, Georgiou P, 2020, Personalised glucose prediction via deep multitask networks, Pages: 110-114, ISSN: 1613-0073

Glucose control is an essential requirement in primary therapy for diabetes management. Digital approaches to maintaining tight glycaemic control, such as clinical decision support systems and artificial pancreas systems rely on continuous glucose monitoring devices and self-reported data, which is usually improved through glucose forecasting. In this work, we develop a multitask approach using convolutional recurrent neural networks (MTCRNN) to provide short-term forecasts using the OhioT1DM dataset which comprises 12 participants. We obtain the following results - 30 min: 19.79±0.06 mg/dL (RMSE); 13.62±0.05 mg/dL (MAE) and 60 min: 33.73±0.24 mg/dL (RMSE); 24.54±0.15 mg/dL (MAE). Multitask learning facilitates an approach that allows for learning with the data from all available subjects, thereby overcoming the common challenge of insufficient individual datasets while learning appropriate individual models for each participant.

Conference paper

Avari P, Uduku C, George D, Herrero P, Reddy M, Oliver Net al., 2019, Differences for Percentage Times in Glycemic Range Between Continuous Glucose Monitoring and Capillary Blood Glucose Monitoring in Adults with Type 1 Diabetes: Analysis of the REPLACE-BG Dataset., Diabetes Technol Ther

Background: Self-monitored blood glucose (SMBG) and real-time continuous glucose monitoring (rtCGM) are used by people living with type 1 diabetes (T1D) to assess glucose and inform decision-making. Percentage time in range (%TIR) between 3.9 and 10 mmol/L has been associated with incident microvascular complications using historical SMBG data. However, the association between %TIR calculated from rtCGM data has not been identified. This study investigates whether %TIR values generated from rtCGM and SMBG data significantly differ from each other in adults with T1D. Materials and Methods: rtCGM and SMBG data from the REPLACE-BG study were obtained and analyzed. The dataset contained rtCGM (Dexcom G4 Platinum) and SMBG (Contour Next) values for 226 participants during a run-in phase lasting up to 10 weeks, followed by the 26-week trial. Percentages times in hypoglycemic, euglycemia and hyperglycemic ranges were generated from rtCGM and SMBG data using last observation carry forward method (zero-order hold) and linear interpolation (first-order hold). Results: Participants had a median (interquartile range [IQR]) age of 43.0 (31.0-55.0) years, and hemoglobin A1C of 53 (49-57) mmol/mol [7.0 (6.6-7.4)%]. The median (IQR) %TIR was significantly higher with rtCGM than with SMBG; 63.0 (55.9-71.0)% versus 54.6 (45.6-63.0)%, respectively, P < 0.001. Median %times in hypoglycemia and hyperglycemia were significantly different with SMBG than rtCGM (P < 0.001). SMBG-derived data using linear interpolation significantly differed from the carry forward method (P < 0.001 for all glycemic ranges). Differences reported were greater at night than during the day (P < 0.001 for all glycemic ranges). Conclusion: The %time in all glycemic ranges reported by SMBG an

Journal article

Herrero P, El-Sharkawy M, Daniels J, Jugnee N, Uduku CN, Reddy M, Oliver N, Georgiou Pet al., 2019, The bio-inspired artificial pancreas for type 1 diabetes control in the home: System architecture and preliminary results, Journal of Diabetes Science and Technology, Vol: 13, Pages: 1017-1025, ISSN: 1932-2968

BACKGROUND: Artificial pancreas (AP) technology has been proven to improve glucose and patient-centered outcomes for people with type 1 diabetes (T1D). Several approaches to implement the AP have been described, clinically evaluated, and in one case, commercialized. However, none of these approaches has shown a clear superiority with respect to others. In addition, several challenges still need to be solved before achieving a fully automated AP that fulfills the users' expectations. We have introduced the Bio-inspired Artificial Pancreas (BiAP), a hybrid adaptive closed-loop control system based on beta-cell physiology and implemented directly in hardware to provide an embedded low-power solution in a dedicated handheld device. In coordination with the closed-loop controller, the BiAP system incorporates a novel adaptive bolus calculator which aims at improving postprandial glycemic control. This paper focuses on the latest developments of the BiAP system for its utilization in the home environment. METHODS: The hardware and software architectures of the BiAP system designed to be used in the home environment are described. Then, the clinical trial design proposed to evaluate the BiAP system in an ambulatory setting is introduced. Finally, preliminary results corresponding to two participants enrolled in the trial are presented. RESULTS: Apart from minor technical issues, mainly due to wireless communications between devices, the BiAP system performed well (~88% of the time in closed-loop) during the clinical trials conducted so far. Preliminary results show that the BiAP system might achieve comparable glycemic outcomes to the existing AP systems (~73% time in target range 70-180 mg/dL). CONCLUSION: The BiAP system is a viable platform to conduct ambulatory clinical trials and a potential solution for people with T1D to control their glucose control in a home environment.

Journal article

Liu C, Vehí J, Avari P, Reddy M, Oliver N, Georgiou P, Herrero Pet al., 2019, Long-term glucose forecasting using a physiological model and deconvolution of the continuous glucose monitoring signal, Sensors, Vol: 19, Pages: 1-19, ISSN: 1424-8220

(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose–insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min

Journal article

Moscardo V, Herrero P, Diez J-L, Gimenez M, Rossetti P, Georgiou P, Bondia Jet al., 2019, Coordinated dual-hormone artificial pancreas with parallel control structure, COMPUTERS & CHEMICAL ENGINEERING, Vol: 128, Pages: 322-328, ISSN: 0098-1354

Journal article

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