122 results found
Lopez B, Martin C, Vinas PH, 2018, Special section on artificial intelligence for diabetes, ARTIFICIAL INTELLIGENCE IN MEDICINE, Vol: 85, Pages: 26-27, ISSN: 0933-3657
Rawson T, o'hare D, Herrero P, et al., 2018, Delivering precision antimicrobial therapy through closed-loop control systems, Journal of Antimicrobial Chemotherapy, Vol: 73, Pages: 835-843, ISSN: 0305-7453
Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients.
Ramkissoon CM, Herrero P, Bondia J, et al., 2018, Unannounced meals in the artificial pancreas: detection using continuous glucose monitoring, Sensors (Basel, Switzerland), Vol: 18, ISSN: 1424-2818
The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the same meal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 ± 2%, 93 ± 5%, and 47 ± 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomes.
Herrero P, Bondia J, Giménez M, et al., 2018, Automatic adaptation of Basal insulin using sensor-augmented pump therapy, Journal of Diabetes Science and Technology, Vol: 12, Pages: 282-294, ISSN: 1932-2968
BACKGROUND: People with insulin-dependent diabetes rely on an intensified insulin regimen. Despite several guidelines, they are usually impractical and fall short in achieving optimal glycemic outcomes. In this work, a novel technique for automatic adaptation of the basal insulin profile of people with diabetes on sensor-augmented pump therapy is presented. METHODS: The presented technique is based on a run-to-run control law that overcomes some of the limitations of previously proposed methods. To prove its validity, an in silico validation was performed. Finally, the artificial intelligence technique of case-based reasoning is proposed as a potential solution to deal with variability in basal insulin requirements. RESULTS: Over a period of 4 months, the proposed run-to-run control law successfully adapts the basal insulin profile of a virtual population (10 adults, 10 adolescents, and 10 children). In particular, average percentage time in target [70, 180] mg/dl was significantly improved over the evaluated period (first week versus last week): 70.9 ± 11.8 versus 91.1 ± 4.4 (adults), 46.5 ± 11.9 versus 80.1 ± 10.9 (adolescents), 49.4 ± 12.9 versus 73.7 ± 4.1 (children). Average percentage time in hypoglycemia (<70 mg/dl) was also significantly reduced: 9.7 ± 6.6 versus 0.9 ± 1.2 (adults), 10.5 ± 8.3 versus 0.83 ± 1.0 (adolescents), 10.9 ± 6.1 versus 3.2 ± 3.5 (children). When compared against an existing technique over the whole evaluated period, the presented approach achieved superior results on percentage of time in hypoglycemia: 3.9 ± 2.6 versus 2.6 ± 2.2 (adults), 2.9 ± 1.9 versus 2.0 ± 1.5 (adolescents), 4.6 ± 2.8 versus 3.5 ± 2.0 (children), without increasing the percentage time in hyperglycemia. CONCLUSION: The present study shows the potential of a novel technique to effectively adjust the basal insulin profile of a type 1 diab
Moscardo V, Diez JL, Herrero P, et al., 2018, SLIDING MODE REFERENCE CONDITIONING DUAL HORMONE COORDINATED GLUCOSE CONTROL, Publisher: MARY ANN LIEBERT, INC, Pages: A86-A86, ISSN: 1520-9156
Tanzanakis A, Herrero P, Georgiou P, 2018, A NOVEL ADAPTIVE ARTIFICIAL PANCREAS USING REINFORCEMENT LEARNING AND ITERATIVE LEARNING CONTROL: AN IN SILICO VALIDATION, Publisher: MARY ANN LIEBERT, INC, Pages: A61-A61, ISSN: 1520-9156
Liu C, Oliver N, Georgiou P, et al., 2018, GLUCOSE FORECASTING USING A PHYSIOLOGICAL MODEL AND STATE ESTIMATION, Publisher: MARY ANN LIEBERT, INC, Pages: A76-A77, ISSN: 1520-9156
Hernandez Perez B, Herrero Viñas P, Miles Rawson T, et al., 2017, Supervised Learning for Infection Risk Inference Using Pathology Data, BMC Medical Informatics and Decision Making, Vol: 17, ISSN: 1472-6947
Background: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions.Methods: From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using four-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions. Results: More than 50\% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available.Conclusion: The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clini
Herrero P, Bondia J, Oliver N, et al., 2017, A coordinated control strategy for insulin and glucagon delivery in type 1 diabetes, Computer Methods in Biomechanics and Biomedical Engineering, Vol: 20, Pages: 1474-1482, ISSN: 1025-5842
Type 1 diabetes is an autoimmune condition characterised by a pancreatic insulin secretion deficit, resulting in high blood glucose concentrations, which can lead to micro- and macrovascular complications. Type 1 diabetes also leads to impaired glucagon production by the pancreatic α-cells, which acts as a counter-regulatory hormone to insulin. A closed-loop system for automatic insulin and glucagon delivery, also referred to as an artificial pancreas, has the potential to reduce the self-management burden of type 1 diabetes and reduce the risk of hypo- and hyperglycemia. To date, bihormonal closed-loop systems for glucagon and insulin delivery have been based on two independent controllers. However, in physiology, the secretion of insulin and glucagon in the body is closely interconnected by paracrine and endocrine associations. In this work, we present a novel biologically-inspired glucose control strategy that accounts for such coordination. An in silico study using an FDA-accepted type 1 simulator was performed to evaluate the proposed coordinated control strategy compared to its non-coordinated counterpart, as well as an insulin-only version of the controller. The proposed coordinated strategy achieves a reduction of hyperglycemia without increasing hypoglycemia, when compared to its non-coordinated counterpart.
Ramkissoon CM, Herrero P, Bondia J, et al., 2017, Meal Detection in the Artificial Pancreas: Implications During Exercise, 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), Publisher: ELSEVIER SCIENCE BV, Pages: 5462-5467, ISSN: 2405-8963
Herrero P, Bondia J, Adewuyi O, et al., 2017, Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability, Computer Methods and Programs in Biomedicine, Vol: 146, Pages: 125-131, ISSN: 0169-2607
Background and ObjectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.MethodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.ResultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; perce
Herrero Vinas P, Pesl P, Reddy M, et al., 2017, Atomatic adjustment of Basal insulin infusion rates in type 1 diabetes using run-to-run control and case-based reasoning, Artificial Intelligence in Medicine
People with type 1 diabetes mellitus rely on a basal-bolus insulinregimen to roughly emulate how a non-diabetic person’s body delivers insulin.Adjusting such regime is a challenging process usually conducted by an expertclinical. Despite several guidelines exist for such purpose, they are usuallyimpractical and fall short in achieving optimal glycemic outcomes. Therefore,there is a need for more automated and efficient strategies to adjust such regime.This paper presents, and in silico validates, a novel technique to automaticallyadapt the basal insulin profile of a person with person with type 1 diabetes. Thepresented technique, which is based on Run-to-Run control and Case-BasedReasoning, overcomes some of the limitations of previously proposedapproaches and has been proved to be robust in front of realistic intra-dayvariability. Over a period of 5 weeks on 10 virtual adult subjects, a significantreduction on the percentage of time in hyperglycemia (<70mg/dl) (from 14.3±5.6to 1.6±1.7, p< 0.01), without a significant increase on the percentage of time inhypoglycemia (>180mg/dl) (from 10.2±5.9 to 1.6±1.7, p=0.1), was achieved.
Rawson T, moore L, Hernandez B, et al., 2017, A systematic review of clinical decision support systems for antimicrobial management: Are we failing to investigate these interventions appropriately?, Clinical Microbiology and Infection, Vol: 23, Pages: 524-532, ISSN: 1469-0691
ObjectivesClinical decision support systems (CDSS) for antimicrobial management can support clinicians to optimise antimicrobial therapy. We reviewed all original literature (qualitative and quantitative) to understand the current scope of CDSS for antimicrobial management and analyse existing methods used to evaluate and report such systems. MethodPRISMA guidelines were followed. Medline, EMBASE, HMIC Health and Management, and Global Health databases were searched from 1st January 1980 to 31st October 2015. All primary research studies describing CDSS for antimicrobial management in adults in primary or secondary care were included. For qualitative studies, thematic synthesis was performed. Quality was assessed using Integrated quality Criteria for the Review Of Multiple Study designs (ICROMS) criteria. CDSS reporting was assessed against a reporting framework for behaviour change intervention implementation.ResultsFifty-eight original articles were included describing 38 independent CDSS. The majority of systems target antimicrobial prescribing (29/38;76%), are platforms integrated with electronic medical records (28/38;74%), and have rules based infrastructure providing decision support (29/38;76%). On evaluation against the intervention reporting framework, CDSS studies fail to report consideration of the non-expert, end-user workflow. They have narrow focus, such as antimicrobial selection, and use proxy outcome measures. Engagement with CDSS by clinicians was poor.ConclusionGreater consideration of the factors that drive non-expert decision making must be considered when designing CDSS interventions. Future work must aim to expand CDSS beyond simply selecting appropriate antimicrobials with clear and systematic reporting frameworks for CDSS interventions developed to address current gaps identified in the reporting of evidence.
Hernandez B, Herrero P, Rawson TM, et al., 2017, Data-drivenWeb-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits and Limitations, 10th International Joint Conference on Biomedical Engineering Systems and Technologies, Publisher: SCITEPRESS, Pages: 119-127
BACKGROUND: Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D). METHODS: We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire. RESULTS: Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change. CONCLUSIONS: Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.
Rawson T, Charani E, Moore L, et al., 2016, Vancomycin therapy in secondary care; investigating factors that impact therapeutic target attainment, Journal of Infection, Vol: 74, Pages: 320-324, ISSN: 1532-2742
Rawson T, Charani E, Moore L, et al., 2016, Mapping the decision pathways of acute infection management in secondary care among UK medical physicians: a qualitative study, BMC Medicine, Vol: 14, ISSN: 1741-7015
BackgroundThe inappropriate use of antimicrobials drives antimicrobial resistance. We conducted a study to map physician decision making processes for acute infection management in secondary care to identify potential targets for quality improvement interventions.MethodsNewly qualified to Consultant level physicians participated in semi-structured interviews. Interviews were audio recorded and transcribed verbatim for analysis using NVIVO11.0 software. Grounded theory methodology was applied. Analytical categories were created using constant comparison approach to the data and participants were recruited to the study until thematic saturation was reached. ResultsTwenty physicians were interviewed. The decision pathway for the management of acute infections follows a Bayesian-like step-wise approach, with information processed and systematically added to prior assumptions to guide management. The main emerging themes identified as determinants of the decision making of individual physicians were; (i) perceptions of providing “optimal” care for the patient with infection by providing rapid and often intravenous therapy; (ii) perceptions that stopping/de-escalating therapy was a senior doctor decision with junior trainees not expected to contribute; (iii) expectation of interactions with local guidelines and microbiology service advice. Feedback on review of junior doctor prescribing decisions was often lacking, causing frustration and confusion on appropriate practice within this cohort. ConclusionInterventions to improve infection management must incorporate mechanisms to promote distribution of responsibility for decisions made. The disparity between expectations of prescribers to start but not review/stop therapy requires addressing urgently with mechanisms to improve communication and feedback to junior prescribers to facilitate their continued development as prudent antimicrobial prescribers.
Reddy M, Pesl P, Xenou M, et al., 2016, Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study, Diabetes Technology & Therapeutics, Vol: 18, Pages: 487-493, ISSN: 1557-8593
Background: The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks.Methods: Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia.Results: Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0–8.2) in week 1 versus 2.0 (0.5–6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study.Conclusion: The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.
Georgiou P, Pesl P, Oliver N, et al., 2016, An Advanced Insulin Bolus Calculator for Type 1 Diabetes, Wireless Medical Systems and Algorithms Design and Applications, Publisher: CRC Press, ISBN: 9781498700788
Design and Applications Pietro Salvo, Miguel Hernandez-Silveira ... VLSI: Circuits for Emerging Applications Tomasz Wojcicki Wireless Medical Systems and Algorithms: Design and Applications ... Wireless Technologies: Circuits, Systems, and Devices Krzysztof Iniewski Wireless Transceiver Circuits: System Perspectives ...
Reddy M, Pesl P, Xenou M, et al., 2016, CLINICAL SAFETY AND FEASIBILITY OF THE ADVANCED BOLUS CALCULATOR FOR TYPE 1 DIABETES BASED ON CASE-BASED REASONING: A 6-WEEK NON-RANDOMISED SINGLE-ARM PILOT STUDY, DIABETES TECHNOLOGY & THERAPEUTICS, Vol: 18, Pages: A34-A35, ISSN: 1520-9156
El Sharkawy M, Herrero P, Reddy M, et al., 2016, A LOW-POWER BIO-INSPIRED ARTIFICIAL PANCREAS, Publisher: MARY ANN LIEBERT, INC, Pages: A54-A54, ISSN: 1520-9156
Ramkissoon CM, Herrero P, Bondia J, et al., 2016, AUTOMATIC DETECTION OF EXERCISE IN PEOPLE WITH TYPE 1 DIABETES USING AN UNSCENTED KALMAN FILTER, Publisher: MARY ANN LIEBERT, INC, Pages: A56-A56, ISSN: 1520-9156
Pesl P, Herrero P, Reddy M, et al., 2016, AUGMENTING AN ADVANCED BOLUS CALCULATOR WITH CONTINUOUS GLUCOSE MONITORING AND A SMARTWATCH, Publisher: MARY ANN LIEBERT, INC, Pages: A97-A97, ISSN: 1520-9156
Pesl P, Herrero P, Reddy M, et al., 2016, GLUCOSE RATE-OF-CHANGE AT MEAL TIMES FOR INSULIN DOSING DECISION SUPPORT, Publisher: MARY ANN LIEBERT, INC, Pages: A97-A97, ISSN: 1520-9156
Herrero P, Bondia J, Amparo G, et al., 2016, A BIHORMONAL GLUCOSE CONTROLLER BASED ON THE PARACRINE INTERACTION BETWEEN BETA CELL AND ALPHA CELL, Publisher: MARY ANN LIEBERT, INC, Pages: A57-A58, ISSN: 1520-9156
Seechurn S, Reddy M, Jugnee N, et al., 2016, Does the addition of glucagon to the closed loop insulin pump add any benefit?, ATTD 2016 9th International Conference on Advanced Technologies & Treatments for Diabetes, Publisher: Mary Ann Liebert, Pages: A61-A61, ISSN: 1520-9156
Herrero P, Delaunay B, Jaulin L, et al., 2016, Robust set-membership parameter estimation of the glucose minimal model, INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Vol: 30, Pages: 173-185, ISSN: 0890-6327
Herrero P, El-Sharkawy M, Pesl P, et al., 2016, Live Demonstrator: Challenging the Bio-inspired Artificial Pancreas with a Mixed-Meal Model Library, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1444-1444, ISSN: 0271-4302
Pesl P, Herrero P, Reddy M, et al., 2016, Live Demonstration: Smartwatch Implementation of an Advanced Insulin Bolus Calculator for Diabetes, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2370-2370, ISSN: 0271-4302
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