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Type 1 diabetes mellitus is a chronic disease that requires those affected to self-administer insulin to control their blood glucose level. However, the estimation of the correct insulin dosage is not easy due to the complexity of glucose metabolism, which usually leads to blood glucose levels far from the optimal. This paper presents an adaptive and personalised basal insulin recommender system based on Kalman filter theory that can be used with or without continuous glucose monitoring systems. The proposed approach is tested with the UVa/PADOVA simulator with eleven virtual adult subjects. It has been tested in combination with two different bolus calculators, and the performance achieved has been compared with that obtained with the default basal doses of the simulator, which can be assumed as optimal. The achieved results demonstrate that the proposed system rapidly converges to the optimal basal dose, and it can be used with adaptive bolus calculators without the risk of instability ; 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

IMEKO TC4 (22è : 2017 : Iasi, Romania). 22nd IMEKO TC4 Symposium and 20th International Workshop on ADC Modelling and Testing: supporting worl development through electrical & electronic mesuraments: September 14-15, 2017, Iasi, Romania ; The paper presents a system which collects medical data from people with type 1 diabetes mellitus, stores the data into a cloud database and implements web pages for data analysis. This system architecture gives possibility to clinicians and patients to share information about patient evolution without the need for the patient to physically visit the hospital. The system includes a clinical decision support system which helps the patient on insulin doses calibration ; This material is based upon the work which is supported by the European Union through the H2020 "PEPPER" project: Patient Empowerment through Predictive Personalized decision support, http://www.pepper.eu.com

IMEKO TC4 (22è : 2017 : Iasi, Romania). 22nd IMEKO TC4 Symposium and 20th International Workshop on ADC Modelling and Testing: supporting worl development through electrical & electronic mesuraments: September 14-15, 2017, Iasi, Romania ; Comunicació de congrés presentada a: IMEKO TC4 Symposium (22nd: 14-15 setembre 2017: Iasi, Romania) and International Workshop on ADC Modelling and Testing (20th: 14-15 setembre 2017: Iasi, Romania) ; The paper presents a system which collects medical data from people with type 1 diabetes mellitus, stores the data into a cloud database and implements web pages for data analysis. This system architecture gives possibility to clinicians and patients to share information about patient evolution without the need for the patient to physically visit the hospital. The system includes a clinical decision support system which helps the patient on insulin doses calibration ; This material is based upon the work which is supported by the European Union through the H2020 "PEPPER" project: Patient Empowerment through Predictive Personalized decision support, http://www.pepper.eu.com

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

Comunicació 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) ; 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

Comunicació presentada a: Workshop on Artificial Intelligence for Diabetes (2nd: 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).

Comunicació 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) ; 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

Comunicació presentada a Workshop on Artificial Intelligence for Diabetes (2nd: 2017: Viena, Àustria) ; 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).

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).

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)

The aim of this article is to propose and examine a quantitative method of determining the degree of compatibility between municipal services. Provision of services and facilities maintenance are usually two biggest expenditures of local governments. Traditionally, facilities host only one service, whereas the challenge and opportunity lies in combining various, compatible services and offering them together under one roof. Such a combination decreases municipal expenditure and has a strong positive impact on the general service quality. For this purpose, we take advantage of the City-block distance formula to calculate the degree of compatibility between municipal services. The method is examined and discussed on a sample of 30 real municipal services. This allows us to find possible combinations of strongly compatible services that should be offered together in Multi-Service Facilities and, at the same time, avoid an unwanted combination of services that are incompatible

Machine learning techniques are the cornerstone to handle the amounts of information available for building comprehensive models for decision support in medical practice. However, the datasets use to have a lot of missing information. In this work we analyse how the random forests technique could be used for dealing with missing phenotype values in order to prognosticate diabetes type 2.

Synergies between multiple energy vectors can support decarbonization of local energy islands and, at the same time, relieve stress from the electricity grid. Multi-vector energy systems offer flexibility to integrate variable and economic local energy generation. However, the implementation and operation of multi-vector energy systems face technical, societal and business-related challenges. This paper identifies these challenges and proposes different tools to tackle them. These tools are to be developed in the E-LAND project funded by European Union under the H2020 scheme. Special focus of the paper is on developing a method for optimal planning and operation of multi-vector grid considering robustness. Another novelty of technical tool lies in making scheduling decisions on long-term and short-term storage considering demand response as flexibility resource. By combining technological, societal and business tools, the project expects to create multi-vector energy systems accepted by citizens and with viable business model ; E-LAND project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 824388

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.