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PHC-28-2015, Grant Agreement 689810
1/02/2016 – 30/01/2019
Beatriz López
beatriz.lopez@udg.edu
+(34) 972.41.88.80.
Description:
PEPPER is a personalised decision support system for chronic disease management that will make predictions based on real-time data in order to empower individuals to participate in the self-management of their disease.
The design will involve users at every stage to ensure that the system meets patient needs and raises clinical outcomes by preventing adverse episodes and improving lifestyle, monitoring and quality of life. Research will be conducted into the development of an innovative adaptive decision support system based on case-based reasoning combined with predictive computer modelling. The tool will offer bespoke advice for self- management by integrating personal health systems with broad and various sources of physiological, lifestyle, environmental and social data. The research will also examine the extent to which human behavioural factors and usability issues have previously hindered the wider adoption of personal guidance systems for chronic disease self-management. It will be developed and validated initially for people with diabetes on basal-bolus insulin therapy, but the underlying approach can be adapted to other chronic diseases.
There will be a strong emphasis on safety, with glucose predictions, dose advice, alarms, limits and uncertainties communicated clearly to raise individual awareness of the risk of adverse events such as hypoglycaemia or hyperglycaemia. The outputs of this research will be validated in an ambulatory setting and a key aspect will be innovation management. All components will adhere to medical device standards in order to meet regulatory requirements and ensure interoperability, both with existing personal health systems and commercial products. The resulting architecture will improve interactions with healthcare professionals and provide a generic framework for providing adaptive mobile decision support, with innovation capacity to be thereby increasing the impact of the project.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689810. The project seeks to develop a personalised decision support system for chronic disease management. The system, which combines case-based reasoning with predictive computer modelling, will empower patients to self-manage their diseases such as diabetes. The 3-year project is going since February 1st, 2016. PEPPER is coordinated by Oxford Brookes University. The other members in the consortium are Imperial College of Science, Technology and Medicine, Universitat de Girona, Institut d’Investigació Biomèdica de Girona, Romsoft SRL and Cellnovo Limited. For more information, please contact the Main Researcher (see below).
Este proyecto está subvencionado por el Programa de Investigación e Innovación Horizonte 2020 de la Unión Europea (Grant Agreement Nº 689810). El proyecto contempla el desarrollo de un sistema de soporte a la decisón personalizado para la gestión de enferemedades crónicas. el sistema, basado enrazonamiento basado en casos y modelos predictivos, permitirá a los pacientes a gestionar por si mismos sus enfermedades, como es el caso de la diabetes. La duración del proyecto, que comenzó en febrero de 2016, es de 3 años. PEPPER está coordinado por la Universidad de Oxford Brookes. Imperial College, Universidad de Girona, Institut d’Investigació Biomèdica de Girona, Romsoft SRL y Cellnovo Limited conforman el resto de consorcio europeo. Para más información , por favor, contacte con el Investigador Principal (véase abajo).
Trust and contextual engagement with the PEPPER system: the qualitative findings of a clinical feasibility study.
Waite, Marion; Aldea, Arantza; Avari, Parizad; Duce, David; Herrero, Pau; Jugnee, Narvada; Leal, Yenny, Lopez, Beatriz; Martin, Clare; Oliver, Nick; Reddy, Monika.
ATTD 2020.
Year: 2020
LINK: https://radar.brookes.ac.uk/radar/items/784eb78c-3b05-43c8-80fa-cf0cdc603589/1/
Visualizing Usage Data from a Diabetes Management System.
Duce, David; Martin, Clare, Russell, Alex; Brown, Dan; Aldea, Arantza; Alshaigy, Bedour; Harrison, Rachel; Waite, Marion; Leal, Yenny, Wos, Marzena, Fernandez-Balsells, Mercè; Fernández-Real, José Manuel, Nita, Lucian; Lopez, Beatrix; Massana, Joaquim; Avari, Parizad; Herrero, Pau; Jugnee, Narvada; Oliver, Nick; Reddy, Monika.
38th Computer Graphics & Visual Computing Conference (CGVC 2020).
Year: 2020
DOI: https://doi.org/10.2312/cgvc.20201144
The PEPPER System Application Program Interface.
Herrero, Pau; Massana, Joaquim; Leal, Yenny; Nita, Lucian; Parizad, Avari; Duce, David; Aldea, Arantza; Georgiou, Pantelis; Fernández-Real, José Manuel; Fernández-Balsells, Mercè; Oliver, Nick, López, Beatriz; Martin, Clare.
ATTD 2020.
Poster.
Year: 2020
Link: http://hdl.handle.net/10256/17821
Safety and feasibility of the PEPPER adaptive bolus advisor and safety system; a randomized control study.
Avari, Parizad; Leal, Yenny; Herrero, Pau; Wos, Marzena; Jugnee, Narvada; Arnoriaga-Rodríguez, María; Thomas, Maria; Chengyuan Liu; Massana, Joaquim; Lopez, Beatriz; Nita, Lucian; Martin, Clare; Fernández-Real, José Manuel; Oliver, Nick; Fernández-Balsells, Mercè; Reddy, Monika.
Diabetes Technology and Therapeutics.
Year: 2020
DOI: http://doi.org/10.1089/dia.2020.0301
Efficacy and safety of the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system: an open-label randomised controlled trial
Parizad Avari, Yenny Teresa Leal Montcada, Marzena Wos, Narvada Jugnee, Maria Thomas, Joaquim Massana i Raurich, Beatriz López Ibáñez, Lucian Nita, Clare Martin, Pau Herrero i Viñas, Nick Oliver, José Manual Fernández-Real, Monika Reddy, Mercè Fernández-Balsells.
ATTD 2020
Madrid, 2020
You can read the full Poster: https://zenodo.org/record/3776870
Personalised Adaptive CBR Bolus Recommender System for Type 1 Diabetes.
Ferran Torrent-Fontbona, Beatriz Lopez.
IEEE Journal of Biomedical and Health Informatics , Vol 23, Num. 1, January 2019, pp. 387-394. Accepted March 2018.
Picture in the issue cover
DOI: https://doi.org/10.1109/JBHI.2018.2813424
Application of Usability Engineering to the Development of a Personalised Decision Support System for Type 1 Diabetes Self-Management.
Clare Martin, Arantza Aldea, Bedour Alshaigy, Parizad Avari, David Duce, Mercè Fernández-Balsells, José Manuel Fernández-Real, Rachel Harrison, Pau Herrero, Narvada Jugnee, Chengyuan Lui, Beatriz López, Joaquim Massana, Yenny Leal, Monika Reddy, Marion Waite, Marzena Wos and Nick Oliver. ATTD2019,-
Berlin, February 20-23.
Poster.
Year: 2019
Case-base Maintenance of a Personalised and Adaptive CBR Bolus Insulin Recommender System for Type 1 Diabetes.
Ferran Torrent-Fontbona, Joaquim Massana, Beatriz López.
Volume 121, 338-346.
December 2018.
Year: 2019
DOI: https://doi.org/10.1016/j.eswa.2018.12.036
Case-base maintenance of a personalised bolus insulin recommender system for Type 1 Diabetes Mellitus.
Torrent-Fontbona, Ferran; Massana, Joaquim; Lopez, Beatriz.
Joint Workshop on Artificial Intelligence in Health (AIH2018).
SWE – SUECIA.
Year: 2018
Link: http://hdl.handle.net/10256/16213
eXiT Research Group at the University of Girona: Artificial Intelligence and Machine Learning Applied to Medicine and Healthcare.
Lopez, Beatriz; Mordvanyuck, Natalia; Massana, Joaquim; Torrent-Fontbona, Ferran; Caceres, Gerard; Pous, Carles.
I CAEPIA Workshop de Grupos de Investigación Españoles de IA en Biomedicina (IABiomed).
Year: 2019
LINK: http://hdl.handle.net/10256/17832
Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction Type 2 diabetes.
Beatriz López; Ferran Torrent-Fontbona; Ramón Viñas; José Manuel Fernández-Real.
Artificial Intelligence in Medicine.
Volume 85 – page 43-49.
Year: 2018
DOI: https://doi.org/10.1016/j.artmed.2017.09.005
Adaptive basal insulin recommender system based on Kalman filter for type 1 diabetes.
F. Torrent-Fontbona.
Expert Systems with Applications,11:1-7.
Year: 2018
DOI: https://doi.org/10.1016/j.eswa.2018.02.015
Special section on artificial intelligence for diabetes.
Lopez, Beatriz; Martin, Clare; Herrero, Pau.
Volume 85, pages 26-27.
Year: 2018
DOI: http://hdl.handle.net/10256/17871
Prediction of Glucose Level Conditions from Sequential Data.
Natalia Mordvaniuk, Ferran Torrent-Fontbona and Beatriz López.
CCIA 2017,20th International Conference of the Catalan Association for Artificial Intelligence.
Deltebre (Espanya).
October 2017.
Year: 2017
Link: http://eia.udg.edu/~nmordvanyuk/papers/Prediction-of-Glucose-Level-Conditions-from%20Sequential-DataCCIA2017.pdf
Cloud Based Acquisition System for Diabetic Data.
L. Nita and F. Torrent-Fontbona.
2017 IMEKO TC4 Symposium, Iasi, 2017.
Year: 2017
Involving physical activity in insulin recommender systems with the use of wearables.
Beatriz López, Alejandro Pozo, Ferran Torrent-Fontbona.
MIE(Informatics for Health2017).
Year: 2017
Avaluació d’un Sistema personalitzat de suport de decisions d’autogestió de la DM1.
M. Wos, MD, Y. Leal, PhD, M. Fernández-Balsells, PhD, C. Martin, PhD, P. Herrero, PhD, B. López , PhD, W.Ricart, PhD, L.Sojo-Vega, MD, E. Esteve, PhD, E. Loshuertos, MSc, J. Shapley, PhD, L. Nita, PhD, J.M. Fernández-Real, PhD.
XIVè Congrés Associació Catalana de Diabetes
Barcelona, Spain, March 2017.
Poster (In Catalan).
Year: 2017
Prediction of postprandial hypoglycemias from insulin intakes and carbohydrates: analysis and comparison between real and simulated datasets.
Fabien Dubosson, Natalia Mordanyuk, Beatriz Lóopez2, and Michael Schumacher.
2nd Workshop on Artificial Intelligence for Diabetes, MIE, Vienna, 2017.
Year: 2017
Link: http://publications.hevs.ch/index.php/publications/show/2240
A CBR-based bolus recommender system for type 1 diabetes.
Ferran Torrent-Fontbona, Beatriz Lopez, and Alejandro Pozo-Alonso.
2nd Workshop on Artificial Intelligence for Diabetes.
MIE, Vienna, 2017.
Year: 2017
PEPPER: Patient Empowerment Through Predictive Personalised Decision Support. Proc.
Pau Herrero, Beatriz Lopez and Clare Martin.
Proc. ECAI Workshop on Artificial Intelligence for Diabetes, pp. 8-9.
Year: 2016
Handling Missing Phenotype Data with Random Forests for Diabetes Risk Prognosis.
Beatriz López, Ramon Viñas, Ferran Torrent and José Manuel Fernández-Real.
Proc. ECAI Workshop on Artificial Intelligence for Diabetes, pp. 39-42.
Year: 2016
1st Workshop on Artificial Intelligence for Diabetes. 2016.
Lopez, Beatriz; Herrero, Pau; Martin, Clare.
The Hague, Netherlands.
Year: 2016
DOI: https://doi.org/10.5281/zenodo.427542
Proc. ECAI Workshop on Artificial Intelligence for Diabetes
Beatriz Lopez, Pau Herrero and Clare Martin (eds).
Year: 2016
Link: http://www.ecai2016.org/content/uploads/2016/08/W7-AID-2016.pdf