Search

Your search keyword '"Artificial intelligence -- Medical applications"' showing total 3 results
Start Over Descriptor "Artificial intelligence -- Medical applications" Topic case-based reasoning Publisher zenodo
3 results on '"Artificial intelligence -- Medical applications"'

1. Application of Usability Engineering to the Development of a Personalised Decision Support System for Type 1 Diabetes Self-Management

Author

Martin, Clare, Aldea, Arantza, Alshaigy, B., Avari, Parizad, Duce, David, Fernández-Balsells, Mercè, Fernández-Real Lemos, José Manuel, Harrison, R., Herrero i Viñas, Pau, Jugnee, Narvada, Lui, C., López Ibáñez, Beatriz, Massana i Raurich, Joaquim, Leal Moncada, Yenny Teresa, Russell, A., Reddy, Monika, Waite, Marion, Wos, Marzena, and Oliver, Nick

Subjects
Diabetis, Case-based reasoning -- Congresses, Intel·ligència artificial -- Aplicacions a la medicina -- Congressos, Diabetes -- Congresses, Diabetes, Artificial intelligence -- Medical applications -- Congresses, Decision support systems, Raonament basat en casos -- Congressos, Case-based reasoning, Sistemes d'ajuda a la decisió, Sistemes d'ajuda a la decisió -- Congressos, Raonament basat en casos, Intel·ligència artificial -- Aplicacions a la medicina, Decision support systems -- Congresses, health care economics and organizations, Diabetis -- Congressos, Artificial intelligence -- Medical applications
Abstract

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

Published
2019
Full Text
View/download PDF

2. A novel, adaptive decision support system for diabetes self-management using artificial intelligence and mathematical modelling

Author

PEPPER (Patient Empowerment through Predictive PERsonalised decision support), Oxford Brookes University, Imperial college London, Universitat de Girona, and Institut d’Investigació Biomèdica de Girona (IdIBGi)

Subjects
Diabetis, Case-based reasoning -- Congresses, Intel·ligència artificial -- Aplicacions a la medicina -- Congressos, Diabetes, Diabetes -- Congresses, Artificial intelligence -- Medical applications -- Congresses, Sistemes d'ajuda a la decisió -- Congressos, Decision support systems, 3. Good health, Case-based reasoning, Sistemes d'ajuda a la decisió, Raonament basat en casos -- Congressos, Raonament basat en casos, Intel·ligència artificial -- Aplicacions a la medicina, Decision support systems -- Congresses, Artificial intelligence -- Medical applications, Diabetis -- Congressos
Abstract

Pòster de congrés presentat a: ICT 2018: Imagine Digital - Connect Europe (4-6 Desembre: Viena, Austria) PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is a research and innovation 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, Fig. 1), an artificial intelligence technique that adapts to new situations according to past experience. The latter uses a model-based approach (Fig. 2) 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

Published
2019
Full Text
View/download PDF

3. PEPPER: Patient Empowerment Through Predictive Personalised Decision Support

Author

Herrero i Viñas, Pau, López Ibáñez, Beatriz, and Martin, Clare

Subjects
Intel·ligència artificial -- Aplicacions a la medicina -- Congressos, Insulin -- Therapeutic use -- Administration -- Congresses, Diabetes -- Congresses, Artificial intelligence -- Medical applications -- Congresses, Sistemes d'ajuda a la decisió -- Congressos, 030209 endocrinology & metabolism, 02 engineering and technology, Insulina -- Ús terapèutic -- Administració -- Congressos, Decision support systems, 2016-08-29, 03 medical and health sciences, Raonament basat en casos -- Congressos, Insulin -- Therapeutic use -- Administration, Sistemes d'ajuda a la decisió, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, European Union, Horizon 2020, Diabetis, Case-based reasoning -- Congresses, Insulina -- Ús terapèutic -- Administració, Diabetes, PEPPER, 3. Good health, Diabetis -- Congressos, Case-based reasoning, Patient empowerment through predictive personalized decision support, Raonament basat en casos, 020201 artificial intelligence & image processing, Intel·ligència artificial -- Aplicacions a la medicina, Decision support systems -- Congresses, Artificial intelligence -- Medical applications
Abstract

Comunicació de congrés presentada a: Workshop on Artificial Intelligence for Diabetes (AID) (1st: 2016: The Hague, Holanda) i European Conference on Artificial Intelligence (ECAI) (22nd: The Hage, Holanda) Aquest workshop ha rebut finançament del programa d'investigació i innovació EU Horizon 2020 sota el núm. d'ajut 689810 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 This project has received funding from the EU Horizon 2020 research and innovation programme under grant agreement No 689810

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results