Suchergebnisse

PEPPER is a newly-launched three-year research project, funded by the EU Horizon 2020 Framework. It will create a portable personalised decision support system to empower individuals on insulin therapy to self-manage their condition. PEPPER employs Case-Based Reasoning to advise about insulin bolus doses, drawing on various sources of physiological, lifestyle, environmental and social data. It also uses a Model-Based Reasoning approach to maximise users' safety. The system will be integrated with an unobtrusive insulin patch pump and has a patient-centric development approach in order to improve patient self-efficacy and adherence to treatment.

Abstract. With more than 60% of the world's population online, how does our rapidly evolving digital world impact creative processes and outcomes? On the one hand, there is the promise of the shared knowledge and ideas of humanity, readily available at our fingertips, providing numerous starting points from which to develop new ideas. On the other hand, we may be overwhelmed by the volume of information, struggle to find and identify quality information to form the basis of a creative thinking process, and instead fall back on common, accepted ideas. Throughout this article, we place creators and creating in the ubiquitous situated context of searching the World Wide Web (i.e., the Web) and consider the implications for a range of everyday creative thinking processes. Research in this area is surprisingly limited, and a number of suggestions are made to take this area forward as the Web becomes an ever-expanding part of our cognitive ecology.

This special section of this issue of the Artificial Intelligence in Medicine (AIIM) journal originates from the First Workshop on Artificial intelligence for Diabetes (AID 2016) on 30th August 2016. The workshop was part of the 22nd European Conference on Artificial Intelligence (ECAI 2016) in The Hague, Holland. Authors with papers accepted for the workshop were subsequently invited to revise and extend their work for publication in this issue, and a wider call was also announced to attract work from other outstanding researchers in the area. Authors were invited to submit original contributions on the overarching theme of Artificial Intelligence-based solutions to problems associated with diabetes. In particular, papers were sought on topics including Intelligent solutions to empower citizens with self-management of health conditions; Intelligent systems for glucose prediction and alarm generation; clinical decision support tools to deal with the avalanche of data gathered by sensors; data mining approaches for risk prediction and prevention of diabetes comorbidities; as well as community tools, platforms to support research in this area and data sets for benchmarkin ; The workshop was supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 689810 (PEPPER)

Patient Empowerment Through Predictive Personalised Decision Support (PEPPER) es un projecto de investigación que desarrolla un sistema personalizado de soporte de decisiones para la autogestión de la diabetes tipo 1 (DM1). PEPPER proporciona recomendaciones en cuanto a la dosis de bolo de insulina (utilizando el razonamiento basado en casos (CBR), una técnica de inteligencia artificial que se adapta a nuevas situaciones de acuerdo con las experiencias pasadas) e ingesta de carbohidratos, basándose en un modelo informático predictivo que promueve la seguridad, proporcionando además, alarmas de predicción de glucemia, suspensión de infusión de insulina y detección de fallos. El objetivo de este proyecto es evaluar la factibilidad, la seguridad, la usabilidad y la viabilidad del sistema PEPPER.

Patient Empowerment Through Predictive Personalised Decision Support (PEPPER) és un projecte d'investigació que desenvolupa un sistema personalitzat de suport de decisions per a l'autogestió de la diabetis tipus 1 (DM1). PEPPER proporciona recomanacions pel que fa a la dosi de bolus d'insulina (utilitzant el raonament basat en casos (CBR), una tècnica d'intel·ligència artificial que s'adapta a noves situacions d'acord amb les experiències passades) i ingesta de carbohidrats, basant-se en un model informàtic predictiu que promou la seguretat, proporcionant a més, alarmes de predicció de glucèmia, suspensió d'infusió d'insulina i detecció de fallades. L'objectiu d'aquest projecte és avaluar la factibilitat, la seguretat, la usabilitat i la viabilitat del sistema PEPPER.

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 ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 689810.

Pòster de congrés presentat a: ATTD 2020 - 13th International Conference on Advanced Technologies & Treatments for Diabetes", celebrat a Madrid del 19 al 22 de febrer de 2020 ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded H2020 project that provides a personalized decision support system for type 1 diabetes selfmanagement. This work describes the application program interface (API) developed for this purpose ; This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 689810

Pòster de congrés presentat a: ATTD 2020 - 13th International Conference on Advanced Technologies & Treatments for Diabetes", celebrat a Madrid del 19 al 22 de febrer de 2020 ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded H2020 project that provides personalised insulin bolus advice for people with Type 1 diabetes (T1D). Here, we evaluate the safety, feasibility and usability of the PEPPER system compared to a standard bolus calculator ; This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 689810

PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project to develop a personalised clinical decision support system for Type 1 diabetes self-management. The tool provides insulin bolus dose advice and carbohydrate recommendations, tailored to the needs of individuals. The former is determined by Case-Based Reasoning (CBR), an artificial intelligence technique that adapts to new situations according to past experience. The latter uses a predictive computer model that also promotes safety by providing glucose alarms, low-glucose insulin suspension and fault detection.

Pòster de congrés presentat a: International Conference on Advanced Technologies & Treatments for Diabetes (ATTD) (12th: 20-23 Febrer 2019: Berlin) ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project which aims to improve the self-management behaviour of adults with type 1 diabetes (T1D). Human factors and ergonomics play a key role in the development of this system ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 689810

Pòster de congrés presentat a: International Conference on Advanced Technologies & Treatments for Diabetes (ATTD) (10th: 15-18 Febrer 2017: Berlin) ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project to develop a personalised clinical decision support system for Type 1 diabetes self-management. The tool provides insulin bolus dose advice and carbohydrate recommendations, tailored to the needs of individuals. The former is determined by Case-Based Reasoning (CBR), an artificial intelligence technique that adapts to new situations according to past experience. The latter uses a predictive computer model that also promotes safety by providing glucose alarms, low-glucose insulin suspension and fault detection ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 689810