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People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system's response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 689810, www.pepper.eu.com/, PEPPER, and the grant of the University of Girona 20162018 (MPCUdG2016). The work has been developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 20142016)

Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9-10.0 mmol/L [70-180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years, and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1-67.8] % vs. 58.4 [49.6-64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness. Clinical trial registration: ClinicalTrials.gov NCT03849755 The work outlined in this manuscript was developed with the support of the awarded distinction by the Generalitat de Catalunya 2017 SGR 1551, and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689810 (PEPPER)

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: 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

Comunicació presentada al First Joint Workshop on AI in Health (AIH 2018), Stockholm, Sweden, July 13-14, 2018 With the goal of supporting people su ering Type 1 Diabetes Mellitus (T1DM), some mobile applications are being developed based on arti cial intelligence techniques. Some of these applications are based on Case-Based Reasoning methodologies (CBR) due to the advantage regarding a personal, adapted recommendation. However, the amount and quality of the cases in the CBR system will threat the system outcome. Most of the maintenance methods developed deals with classi cation tasks, while recommending an insulin dose (bolus) involves a regression task. In this paper, a new maintenance method presented, with the particularity of dealing with a regression tasks. The method is applied over the Pepper insulin dose recommender system, and tested using the UVA/Padova simulator, exhibiting the improvements of the proposal in terms of both, the person health and the case-base size This research project has been partially funded through BR-UdG Scholarship of the University of Girona granted to Joaquim Massana Raurich. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016), the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450- C2-1-R) and the European Unions Horizon 2020 research and innovation programme under grant agreement No 680708

Comunicació de congrés presentada a: Informatics for health conference (2017: Manchester, UK). Informatics for Health 2017: 24-26 April 2017: Manchester, UK (Paper session: Diabetes ans ageing. Exchange 3. 26 April 2017) Aquest workshop ha rebut finançament del programa d'investigació i innovació EU Horizon 2020 sota el núm. d'ajut 689810 Type Diabetes Mellitus 1 (TDM1 patients are able to determine the amount of insulin to be injected in a dose according to the most recent food ingest and other factors such as physical activity, or menstruation. Recently, other elements such as stress, have been determined as key factors too, which influence this decision. Dealing with all these factors is a complex task, and patients suffering this illness are very active in looking for tools that can help them in these daily decisions. In that regard, insulin recommender systems are decision support system (DSS) designed with the aim of providing the appropriate insulin dose to a given patient in a given moment. Moreover, the deployment of such kind of DSS in mobile devices is offering the opportunity to use new sensors that may provide additional information to improve the recommendations. For example, some researchers are exploring mobile cameras to process the food ingested in order to automatically count the carbohydrates to be considered. Other sensors, like smartwatches or wrist bands offer the opportunity to track patients’ physical activity or even their stress level in order to feed the next insulin recommendation decision with this information. Our work concerns the development of an adaptive recommender system that exploits the information from wearables, in order to improve the recommendation provided to TDM1 patients This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 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

Pòster de congrés presentat a: Congreso Nacional de la Sociedad Española de Diabetes (SED) (28è: 5-7 Abril 2017: Barcelona) 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 Este proyecto ha recibido financiación del programa de investigación e innovación Horizonte 2020 de la UE bajo acuerdo de subvención nº 689810

Pòster de congrés presentat a: Congrés de l' Associació Catalana de Diabetis (ACD), (14è: 16-17 Març 2017: Badalona) 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 Aquest projecte ha rebut finançament del programa de recerca i innovació Horizonte 2020 de la UE sota l’acord de subvenció nº 689810.

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

Clinical Decision Support System (CDSSs) should form an important part of the field of clinical knowledge management technologies through their capacity to support the clinical process and use of knowledge, including knowledge maintenance and continuous learning, from diagnosis and investigation through surgery, treatment and long-term care. The work presented shows a workflow-based CDSS designed to give case-specific assessment to clinicians during complex surgery or Minimally Invasive Surgery (MISs). Following a perioperative workflow, the designed software will use a Case-Based Reasoning (CBR) methodology to retrieve similar past cases from a case base to provide support at any particular point of the process. The graphical user interface allows easy navigation through the whole support progress, from the initial configuration steps to the final results organized as sets of experiments easily visualized in a user-friendly way. The eXiTCDSS tool is presented giving support to a recent complex minimally invasive surgery which is receiving growing attention lately, the Transcatheter Aortic Valve Implantation (TAVI). The results obtained are presented on a basis of a real TAVI case base of 82 patients operated at Rennes University Hospital We would like to give our special thanks to the Hospital Clinic of Barcelona, the Centre Hospitalier Universitaire de Rennes and the Laboratoire Traitement du Signal et de l'Image from the University of Rennes for helping us with the case model definition and giving us example cases of TAVI patients. This work has been financed by the Spanish Government Commission Ministerio de Industria, Turismo y Comercio (MITyC) under the project PLAN AVANZA 2 labeled by Information Technology for European Advancement 2 (ITEA2). Also, this research project has been partially funded through the project labeled DPI2011-24929

Objective: Medical applications have special features (interpretation of results in medical metrics, experiment reproducibility and dealing with complex data) that require the development of particular tools. The eXiT*CBR framework is proposed to support the development of and experimentation with new case-based reasoning (CBR) systems for medical diagnosis. Method: Our framework offers a modular, heterogeneous environment that combines different CBR techniques for different application requirements. The graphical user interface allows easy navigation through a set of experiments that are pre-visualized as plots (receiver operator characteristics (ROC) and accuracy curves). This user-friendly navigation allows easy analysis and replication of experiments. Used as a plug-in on the same interface, eXiT*CBR can work with any data mining technique such as determining feature relevance. Results: The results show that eXiT*CBR is a user-friendly tool that facilitates medical users to utilize CBR methods to determine diagnoses in the field of breast cancer, dealing with different patterns implicit in the data. Conclusions: Although several tools have been developed to facilitate the rapid construction of prototypes, none of them has taken into account the particularities of medical applications as an appropriate interface to medical users. eXiT*CBR aims to fill this gap. It uses CBR methods and common medical visualization tools, such as ROC plots, that facilitate the interpretation of the results. The navigation capabilities of this tool allow the tuning of different CBR parameters using experimental results. In addition, the tool allows experiment reproducibility.

Comunicació de congrés presentada a: Workshop on Artificial Intelligence for Diabetes (2nd: 2017: Viena, Àustria) i Conference on Artificial Intelligence in Medicine (AIME) (16th: 2017: Viena, Àustria) Aquest workshop ha rebut finançament del programa d'investigació i innovació EU Horizon 2020 sota el núm. d'ajut 689810 People with type 1 diabetes mellitus usually need to administer bolus insulin before each meal to keep the blood glucose level in the target glycaemic range. However, the factors involved in the calculation of the appropriate dose can change due to multiple factors and with an unknown relation. This may increase the error in the bolus calculation, and therefore, increase the chances of hypoglycaemia and hyperglycaemia. This paper proposes a bolus recommender system based on case based reasoning developed under project PEPPER, with the objective of recommending personalised and adaptive bolus doses. The system has been tested with in silico adults with UVA/PADOVA T1DM simulator. Results show that the use of the proposed bolus recommender system increases the percentage of time in the target glycaemic range This project has received funding from the grant of the University of Girona 2016-2018 (MPCUdG2016) and the European Union Horizon 2020 research and innovation programme under grant agreement No. 689810, www.pepper.eu.com/, PEPPER. The work has been developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016).

In this work we propose a user-friendly medically oriented tool for prognosis development systems and experimentation under a case-based reasoning methodology. The tool enables health care collaboration practice to be mapped in cases where different doctors share their expertise, for example, or where medical committee composed of specialists from different fields work together to achieve a final prognosis. Each agent with a different piece of knowledge classifies the given cases through metrics designed for this purpose. Since multiple solutions for the same case are useless, agents collaborate among themselves in order to achieve a final decision through a coordinated schema. For this purpose, the tool provides a weighted voting schema and an evolutionary algorithm (genetic algorithm) to learn robust weights. Moreover, to test the experiments, the tool includes stratified cross-validation methods which take the collaborative environment into account. In this paper the different collaborative facilities offered by the tool are described. A sample usage of the tool is also provided This research project has been partially funded through the projects labelled TIN2008-04547, DPI2011-24929 and CTQ2008-06865-C02-02 and the grants UDG-BR10/18 and FPU-AP2009-2831