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Martin, Clare
Aldea, Arantza Brown, Daniel Duce, D. Fernández-Real Lemos, José Manuel Gay Sacristán, Pablo Georgiou, Pantelis Harrison, R. Herrero, Pau Innocenti, Bianca López Ibáñez, Beatriz Leal Moncada, Yenny Teresa Nita, Lucian Pesl, Peter Petite, Roberto Reddy, Monika Shapley, Julian Torrent-Fontbona, Ferran Waite, Marion Wos, Marzena Oliver, Nick |
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2020 February 15 | |
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 |
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http://hdl.handle.net/2072/372519 | |
eng | |
PEPPER (Patient Empowerment through Predictive PERsonalised decision support) | |
Tots els drets reservats | |
Raonament basat en casos
Case-based reasoning Diabetis Diabetes Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Sistemes d’ajuda a la decisió Decision support systems |
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Personalised Clinical Decision Support For Diabetes Management Using Real-time Data [Pòster] | |
info:eu-repo/semantics/conferenceObject | |
Recercat |