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Background Taking care of chronic or long-term patients at home is an arduous task. Non-professional caregivers suffer the consequences of doing so, especially in terms of their mental health. Performing some simple activities through a mobile phone app may improve their mindset and consequently increase their positivity. However, each caregiver may need support in different aspects of positive mental health. In this paper, a method is defined to calculate the utility of a set of activities for a particular caregiver in order to personalize the intervention plan proposed in the app. Methods Based on the caregivers’ answers to a questionnaire, a modular averaging method is used to calculate the personal level of competence in each positive mental health factor. A reward-penalty scoring procedure then assigns an overall impact value to each activity. Finally, the app ranks the activities using this impact value. Results The results of this new personalization method are provided based on a pilot test conducted on 111 caregivers. The results indicate that a conjunctive average is appropriate at the first stage and that reward should be greater than penalty in the second stage. Conclusions The method presented is able to personalize the intervention plan by determining the best order of carrying out the activities for each caregiver, with the aim of avoiding a high level of deterioration in any factor.

Software projects are amongst the most common professional activities of computer engineers. Gaining competences on the design and development of software projects is, however, a complex issue that cannot be tackled within a single subject. In this article, we detail the design and implementation of a teaching methodology that aims at providing a comprehensive simulation of the whole life cycle of software projects throughout the coordination of the practical exercises of subjects of different courses. The proposal consists in developing the same software project in consecutive courses under the perspective of the three main roles involved in the project development: designer, developer and director. In the second course, the students exercise the developer role in teams of 4 people under the supervision of a student of the fourth course, who acts as the director . The director has previously proposed a formal design of the project in the third course ( designer role ). This methodology has been successfully deployed for 4 years in the Computer Engineering Degree at the Universitat Rovira i Virgili. The results show an improvement of the skills and competences related to the three involved subjects, which include better programming quality, better team coordination and fulfilment of deadlines, as well as a much practical view of the director’s role.

Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability. They use to work as intuition machines with high statistical confidence but unable to give interpretable explanations about the reported results. The vast amount of parameters of these models make difficult to infer a rationale interpretation from them. In this paper we present a diabetic retinopathy interpretable classifier able to classify retine images into the different levels of disease severity and of explaining its results by assigning a score for every point in the hidden and input space, evaluating its contribution to the final classification in a linear way. The generated visual maps can be interpreted by an expert in order to compare its own knowledge with the interpretation given by the model., Submitted to Elsevier

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.

A Fuzzy Decision Tree is a classification method consisting of a set of rules defined on fuzzy variables. The final class assignment is done according to the output of all the rules of the tree. Generally, the maximum operator is used to aggregate the results of the rules. However, some approaches based on more complex aggregation operators have appeared recently. In this work we propose to use Sugeno and Choquet integrals together with a Hierarchically ⊥-Decomposable Fuzzy Measure (HDFM) to aggregate the rules' values. The HDFM exploits the hierarchical structure of the fuzzy decision tree and takes into account the confidence value of the output together with the classification ambiguity of the rules. The HDFM is built using Sugeno-Weber t-conorms.We validate this approach on several classification problems and make a comparison of the performance with the state of art aggregation operators. Finally, a case study with a real dataset of diabetic patients is analyzed to predict the risk of suffering from diabetic retinopathy.

Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.

Background: To measure the relationship between variability in HbA1c and microalbuminuria (MA) and diabetic retinopathy (DR) in the long term. Methods: A prospective case-series study, was conducted on 366 Type 1 Diabetes Mellitus patients with normoalbuminuria and without diabetic retinopathy at inclusion. The cohort was followed for a period of 12 years. The Cox survival analysis was used for the multivariate statistical study. The effect of variability in microangiopathy (retinopathy and nephropathy) was evaluated by calculating the standard deviation of HbA1c (SD-HbA1c), the coefficient of variation of HbA1c (CV-HbA1c), average real variability (ARV-HbA1c) and variability irrespective of the mean (VIM-HbA1c) adjusted for the other known variables. Results: A total of 106 patients developed diabetic retinopathy (29%) and 73 microalbuminuria (19.9%). Overt diabetic nephropathy, by our definition, affected only five patients (1.36%). Statistical results show that the current age, mean HbA1c, SD-HbA1c and ARV-HbA1c are significant in the development of diabetic retinopathy. Microalbuminuria was significant for current age, mean HbA1c, CV-HbA1c and ARV-HbA1c. Conclusions: By measuring the variability in HbA1c, we can use SD-HbA1c and ARV-HbA1c as possible targets for judging which patients are at risk of developing DR and MA, and CV-HbA1c as the target for severe DR.

Purpose: To validate a clinical decision support system (CDSS) that estimates risk of diabetic retinopathy (DR) and to personalize screening protocols in type 2 diabetes mellitus (T2DM) patients. Methods: We utilized a CDSS based on a fuzzy random forest, integrated by fuzzy decision trees with the following variables: current age, sex, arterial hypertension, diabetes duration and treatment, HbA1c, glomerular filtration rate, microalbuminuria, and body mass index. Validation was made using the electronic health records of a sample of 101,802 T2DM patients. Diagnosis was made by retinal photographs, according to EURODIAB guidelines and the International Diabetic Retinopathy Classification. Results: The prevalence of DR was 19,759 patients (19.98%). Results yielded 16,593 (16.31%) true positives, 72,617 (71.33%) true negatives, 3165 (3.1%) false positives, and 9427 (9.26%) false negatives, with an accuracy of 0.876 (95% confidence interval [CI], 0.858–0.886), sensitivity of 84% (95% CI, 83.46–84.49), specificity of 88.5% (95% CI, 88.29–88.72), positive predictive value of 63.8% (95% CI, 63.18–64.35), negative predictive value of 95.8% (95% CI, 95.68–95.96), positive likelihood ratio of 7.30, and negative likelihood ratio of 0.18. The type 1 error was 0.115, and the type 2 error was 0.16. Conclusions: We confirmed a good prediction rate for DR from a representative sample of T2DM in our population. Furthermore, the CDSS was able to offer an individualized screening protocol for each patient according to the calculated risk confidence value. Translational Relevance: Results from this study will help to establish a novel strategy for personalizing screening for DR according to patient risk factors. The authors thank Phil Hoddy for his language assistance and for editing and correcting the English text. Supported by Instituto de Investigaciones Carlos III (research projects PI18/00169, PI12/01535, and PI15/01150) and by the European Regional Development Fund (FEDER). The authors thank Phil Hoddy for his language assistance and for editing and correcting the English text. Supported by Instituto de Investigaciones Carlos III (research projects PI18/00169, PI12/01535, and PI15/01150) and by the European Regional Development Fund (FEDER).

Proposal of weighted kappa as loss function for ordinal regression in deep learning.Derivation of equations required for applying first order optimization algorithms.Solve three real-world ordinal classification problem.Performance comparison of the models trained by both log loss and kappa loss.Check stability of the kappa loss function using different type of data. Weighted Kappa is an index of reference used in many diagnosis systems to compare the agreement between different raters. This index can be also used to evaluate the performance of automatic classification methods against the gold standard given by an expert (or from a consensus of an expert group). On the other hand, in the last years, deep learning has achieved a great importance as a new machine learning method. The usual loss function used in deep learning for multi-class classification is the logarithmic loss. In this paper we explore the direct use of a weighted kappa loss function for multi-class classification of ordinal data, also known as ordinal regression. Three classification problems are solved in the paper using these two loss functions. Results confirm that better classification is made when the model is constructed with the optimization of kappa instead of logarithmic loss.

Background/aims To determine the incidence of any diabetic retinopathy (any-DR), sight-threatening diabetic retinopathy (STDR) and diabetic macular oedema (DMO) and their risk factors in type 1 diabetes mellitus (T1DM) over a screening programme. Methods Nine-year follow-up, prospective population-based study of 366 patients with T1DM and 15 030 with T2DM. Epidemiological risk factors were as follows: current age, age at DM diagnosis, sex, type of DM, duration of DM, arterial hypertension, levels of glycosylated haemoglobin (HbA1c), triglycerides, cholesterol fractions, serum creatinine, estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR). Results Sum incidence of any-DR was 47.26% with annual incidence 15.16±2.19% in T1DM, and 26.49% with annual incidence 8.13% in T2DM. Sum incidence of STDR was 18.03% with annual incidence 5.77±1.21% in T1DM, and 7.59% with annual incidence 2.64±0.15% in T2DM. Sum incidence of DMO was 8.46% with annual incidence 2.68±038% in patients with T1DM and 6.36% with annual incidence 2.19±0.18% in T2DM. Cox's survival analysis showed that current age and age at diagnosis were risk factors at p

Background: To validate our deep learning algorithm (DLA) to read diabetic retinopathy (DR) retinographies. Introduction: Currently DR detection is made by retinography; due to its increasing diabe...

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. Therefore, our model could be integrated into daily clinical practice to accelerate the diagnosis of Diabetic Retinopathy due to its simplicity, performance, and speed.

Recommender systems aim to suggest lists of items that match accurately the user’s preferences. In the last years it has been argued that the diversity of the recommendations also plays an important role in the overall satisfaction of the user. Increasing the diversity of the suggestions may be beneficial both for the user and for retailers. This paper provides a brief review of the most popular diversification mechanisms and it introduces two new ones (Cluster Random and Cluster Quadratic) based on the semantic clustering of the domain objects. It also shows how the level of diversification may be dynamically adapted to the variety in the preferences of the user. A thorough evaluation of the diversification mechanisms on a Tourism recommender has been performed, reaching the conclusion that the new Cluster Quadratic diversification method achieves very competitive levels of precision and recall, while keeping an acceptable computational cost.

Diabetic retinopathy (DR) has become a major worldwide health problem due to the increase in blindness among diabetics at early ages. The detection of DR pathologies such as microaneurysms, hemorrhages and exudates through advanced computational techniques is of utmost importance in patient health care. New computer vision techniques are needed to improve upon traditional screening of color fundus images. The segmentation of the entire anatomical structure of the retina is a crucial phase in detecting these pathologies. This work proposes a novel framework for fast and fully automatic blood vessel segmentation and fovea detection. The preprocessing method involved both contrast limited adaptive histogram equalization and the brightness preserving dynamic fuzzy histogram equalization algorithms to enhance image contrast and eliminate noise artifacts. Afterwards, the color spaces and their intrinsic components were examined to identify the most suitable color model to reveal the foreground pixels against the entire background. Several samples were then collected and used by the renowned convexity shape prior segmentation algorithm. The proposed methodology achieved an average vasculature segmentation accuracy exceeding 96%, 95%, 98% and 94% for the DRIVE, STARE, HRF and Messidor publicly available datasets, respectively. An additional validation step reached an average accuracy of 94.30% using an in-house dataset provided by the Hospital Sant Joan of Reus (Spain). Moreover, an outstanding detection accuracy of over 98% was achieved for the foveal avascular zone. An extensive state-of-the-art comparison was also conducted. The proposed approach can thus be integrated into daily clinical practice to assist medical experts in the diagnosis of DR.

This work has been supported by REACTION (contract no. EVK2-CT2002-80025) and CASCADE (FP7, grant agreement no. 283068) projects, funded by the European Commission; DRYEX project, funded by the Spanish Ministry of Economy and Competiveness (CGL2014-59074-R); and ITAKA research group, under the Government of Catalonia grants 2009SGR-01523 and 2014SGR-652.

Background/aims To determine the changes in the incidence of diabetic retinopathy (DR), diabetic macular oedema (DMO) and their risk factors in a population-based study of patients with diabetes mellitus (DM) referred to our 16 Primary Health Care Areas (HCAs). Methods Prospective population-based study of a total of 15 396 Caucasian patients with DM, who represent 86.53% of the total patients with DM in our HCAs, were studied over an 8-year follow-up period. All patients were screened with a mean follow-up of 3.18±1.11 times for each patient over the 8 years. Results The yearly mean value of any DR was 8.37±2.19% (8.09%–8.99%); of advanced DR yearly mean value of 0.46±0.22% (0.03–0.78); and of DMO a yearly mean value of 2.19±0.18% (2%–2.49%). A clear increase was observed in the last 3 years, any DR increased from 8.09% in 2007 to 8.99% in 2014, and DMO from 2% in 2007 to 2.49% in 2014. These increases were more evident in some age groups. For patients with any DR aged 41–50 and 51–60 and for patients with advanced DR aged 41–50, 51–60 and 61–70, the increase was more marked, related to an increase in HbA1c values or to patients treated with insulin. Conclusions An increase in the incidence of DR and DMO was observed, especially in the younger patients aged between 31 and 70 years. This is linked to bad metabolic control of DM. Our results suggest a greater number of ocular complications in the near future, such as neovascular glaucoma, if these current findings are not addressed.

Breast cancer is one of the most dangerous diseases that attack women in their 40s worldwide. Due to this fact, it is estimated that one in eight women will develop a malignant carcinoma during their life. In addition, the carelessness of performing regular screenings is an important reason for the increase of mortality. However, computer-aided diagnosis systems attempt to enhance the quality of mammograms as well as the detection of early signs related to the disease. In this paper we propose a bank of Gabor filters to calculate the mean, standard deviation, skewness and kurtosis features by four-sized evaluation windows. Therefore, an active strategy is used to select the most relevant pixels. Finally, a supervised classification stage using two-class support vector machines is utilised through an accurate estimation of kernel parameters. In order to show the development of our methodology based on mammographic image analysis, two main experiments are fulfilled: abnormal/normal breast tissue classificat...

Global change refers to climate changes, but also demographic, technological and economic changes. Predicted water scarcity will be critical in the coastal Mediterranean region, especially for provision to mid-sized and large-sized cities. This paper studies the case of the city of Tarragona, located at the Mediterranean area of north-eastern Spain (Catalonia). Several scenarios have been constructed to evaluate different sectorial water allocation policies to mitigate the water scarcity induced by global change. Future water supply and demand predictions have been made for three time spans. The decision support system presented is based on the outranking model, which constructs a partial pre-order based on pairwise preference relations among all the possible actions. The system analyses a hierarchical structure of criteria, including environmental and economic criteria. We compare several adaptation measures including alternative water sources, inter-basin water transfer and sectorial demand management c...

Background: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients. Method: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR). Results: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria. Discussion: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS. Conclusions: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

Comunicación presentada al 15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 (15 - 18 october 2018). Fuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno $$\lambda $$ -measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature. This work is supported by the URV grant 2017PFR-URV-B2-60, and by the Spanish research projects no: PI12/01535 and PI15/01150 for (Instituto de Salud Carlos III and FEDER funds). Mr. Saleh has a Pre-doctoral grant (FI 2017) provided by the Catalan government and an Erasmus+ travel grant by URV. Prof. Bustince acknowledges the support of Spanish project TIN2016-77356-P.

Intelligent data analysis methods usually require as input a matrix, in which each row is an object to be analysed and each column is an attribute. In most cases it is assumed that attributes are Boolean, categorical or numerical. With the advent of semantic domain information in the form of ontologies, it is now common to find also semantic attributes, which may take as value a list of concepts. This paper proposes a new ontology-based procedure to compute the similarity between lists of semantic values, which may be used to compare objects. This measure is employed in an enhanced version of the k-means clustering method. The usefulness of the obtained classes has been tested in the context of a Web-based personalised recommender of Tourist destinations.

Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice.

Recommender systems are very useful in domains in which a large amount of continuous information needs to be evaluated before a decision is made. Systems that permanently interact with users need to be adapted to changes in their interests. This paper proposes an algorithm that takes advantage of the preference information implicit in the actions of the user to dynamically adapt the user profile, in which user preferences are represented as fuzzy sets. The algorithm has been tested with real data extracted from the New York Times and has shown promising results. This paper presents the adaptation algorithm and discusses the influence of its basic parameters.

Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.

Background: Prospective, population-based study of an 8-year follow up. To determine the direct cost of diabetic retinopathy [DR], evaluating our screening programme and the cost of treating DR, focusing on diabetic macular oedema [DMO] after anti-vascular endothelial growth factor [anti-VEGF] treatment. Methods: A total of 15,396 diabetes mellitus [DM] patients were studied. We determined the cost-effectiveness of our screening programme against an annual programme by applying the Markov simulation model. We also compared the cost-effectiveness of anti-VEGF treatment to laser treatment for screened patients with DMO. Results: The cost of our 2.5-year screening programme was as follows: per patient with any-DR, €482.85 ± 35.14; per sight-threatening diabetic retinopathy [STDR] patient, €1528.26 ± 114.94; and €1826.98 ± 108.26 per DMO patient. Comparatively, an annual screening programme would result in increases as follows: 0.77 in QALY per patient with any-DR and 0.6 and 0.44 per patient with STDR or DMO, respectively, with an incremental cost-effective ratio [ICER] of €1096.88 for any-DR, €4571.2 for STDR and €7443.28 per DMO patient. Regarding diagnosis and treatment, the mean annual total cost per patient with DMO was €777.09 ± 49.45 for the laser treated group and €7153.62 ± 212.15 for the anti-VEGF group, with a QALY gain of 0.21, the yearly mean cost was €7153.62 ± 212.15 per patient, and the ICER was €30,361. Conclusions: Screening for diabetic retinopathy every 2.5 years is cost-effective, but should be adjusted to a patient's personal risk factors. Treatment with anti-VEGF for DMO has increased costs, but the cost-utility increases to 0.21 QALY per patient.

Diabetic retinopathy is an ocular disease that involves an important healthcare spending and is the most serious cause of secondary blindness. Precocious and precautionary detection through a yearly screening of the eye fundus is difficult to make because of the large number of diabetic patients. This paper presents a novel clinical decision support system, based on fuzzy rules, that calculates the risk of developing diabetic retinopathy. The system has been trained and validated on a dataset of patients from Sant Joan de Reus University Hospital. The system achieves levels of sensitivity and specificity above 80 %, which is in practice the minimum threshold required for the validity of clinical tests.

The regional water allocation planning is one of those complex decision problems where holistic approach to water supply management considering different criteria would be valuable. However, multi-criteria decision making with diverse indicators measured on different scales and uncertainty levels is difficult to solve. Objective of this paper is to develop scenarios for the future imbalances in water supply and demand for a water stressed Mediterranean area of Northern Spain (Tarragona) and to test the applicability and suitability of an outranking method ELECTRE-III-H for evaluating sectoral water allocation policies. This study is focused on the use of alternative water supply scenarios to fulfil the demand of water from three major sectors: domestic, industrial and agricultural. A detail scenario planning for regional water demand and supply has been discussed. For each future scenario of climate change, the goal is to obtain a ranking of a set of possible actions with regards to different types of indicators (costs, water stress and environmental impact). The analytical method used is based on outranking models for decision aid with hierarchical structures of criteria and ranking alternatives using partial preorders based on pairwise preference relations. We compare several adaptation measures including alternative water sources (reclaimed water and desalination); inter basin water transfer and sectoral demand management coming from industry, agriculture and domestic sectors and tested the sustainability of management actions for different climate change scenarios. Results have shown use of alternative water resources as the most reliable alternative with medium reclaimed water reuse in industry and agriculture and low to medium use of desalination water in domestic and industrial sectors as the best alternative. The proposed method has several advantages such as the management of heterogeneous scales of measurement without requiring any artificial transformation and the management of uncertainty by means of comparisons at a qualitative level in terms of the decision maker preferences.

The Multiagent Systems Group (GruSMA) is part of the Research Group on Artificial Intelligence at Spain's University Rovira i Virgili. GruSMA specializes in agent technology, working in application areas such as mobile devices, transportation and tourism user services, automated ontology construction, and agent-based simulation of complex systems. Over the past five years, GruSMA has designed and developed many agent applications to address healthcare problems. Some of these systems are academic, proof-of-concept exercises, but they point to the kind of problems in medicine that intelligent agents can help solve in the near term

* Luis Del Vasto-Terrientes luismiguel.delvasto@urv.cat Aida Valls aida.valls@urv.cat Piotr Zielniewicz piotr.zielniewicz@cs.put.poznan.pl Joan Borras joan.borras@pct-turisme.cat 1 Departament d’Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av Paisos Catalans, 26 43007 Tarragona, Catalonia Spain 2 Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan Poland 3 Parc Cientific Tecnologic de Turisme i Oci, C/ Joanot Martorell, 15 43480 Vila-seca, Catalonia Spain Published online: 2015 (2016) 46:347–348

Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years.

The estimation of semantic similarity between words is an important task in many language related applications. In the past, several approaches to assess similarity by evaluating the knowledge modelled in an ontology have been proposed. However, in many domains, knowledge is dispersed through several partial and/or overlapping ontologies. Because most previous works on semantic similarity only support a unique input ontology, we propose a method to enable similarity estimation across multiple ontologies. Our method identifies different cases according to which ontology/ies input terms belong. We propose several heuristics to deal with each case, aiming to solve missing values, when partial knowledge is available, and to capture the strongest semantic evidence that results in the most accurate similarity assessment, when dealing with overlapping knowledge. We evaluate and compare our method using several general purpose and biomedical benchmarks of word pairs whose similarity has been assessed by human experts, and several general purpose (WordNet) and biomedical ontologies (SNOMED CT and MeSH). Results show that our method is able to improve the accuracy of similarity estimation in comparison to single ontology approaches and against state of the art related works in multi-ontology similarity assessment.

Home Care (HC) assistance is emerging as an effective and efficient alternative to institutionalized care, especially for the case of senior patients that present multiple co-morbidities and require life long treatments under continuous supervision. The care of such patients requires the definition of specially tailored treatments and their delivery involves the coordination of a team of professionals from different institutions, requiring the management of many kinds of knowledge (medical, organizational, social and procedural). The K4Care project aims to assist the HC of elderly patients by proposing a standard HC model and implementing it in a knowledge-driven e-health platform aimed to support the provision of HC services. This paper focuses on two knowledge-based personalization aspects incorporated in the platform that aim to overcome the difficulties of HC delivery. The first one is the assistance to medical practitioners in the process of defining a customized treatment adjusted to the medical and social conditions of a particular patient in order to consider multiple co-morbidities. The second one is the possibility of tailoring the profiles of the care professionals according to the medical and organizational daily requirements in order to allow a flexible care delivery. Those two aspects, guided by the knowledge explicitly represented in the platform, play a crucial role in the medical and social acceptance of this kind of e-health systems. The paper also includes a real case study designed and tested by healthcare professionals and includes encouraging results from the test of the platform in a real health care environment in the city of Pollenza (Italy).

Ontologies define a set of concepts related to a certain domain as well as the relationships among them. This structure may be exploited to represent and reason about the preferences of a user. The user profile stores the degrees of interest of the user on several concepts using membership functions. In this way, each concept in the ontology is a fuzzy set and any user belongs to this fuzzy set to a certain degree. To represent the uncertainty on this information, a degree of confidence on the membership value is also included. After an initial assignment of preferences, spreading algorithms that exploit the taxonomical information of the ontology are applied to propagate the information about the user’s preferences (and their associated uncertainty) through the whole set of concepts. This framework for managing uncertain preferences has been successfully applied in a Tourism recommender system.

The development of digital and web technologies opened new horizons regarding the generation of personalized contents and the management of visitor services. The objective of SIGtur/E-Destination is to provide to the tourist and travel sector electronic tools for the sustainable management of destinations, also possibly leading to an increase in the effectiveness of visitor service firms. The SIGtur/E-Destination system includes a warehouse of geo-referenced information that updates the available information on the tourism activities in the Costa Daurada and Terres de l’Ebre in the south of Catalonia (Spain). This catalogue of information on resources, attractions, products, establishments and packages has to make possible the improvement of the interaction of between public and private agents and visitors. A recommender system uses this catalogue to offer personalized information to the tourists.

The version of record is available online at: http://dx.doi.org/10.1007/s10844-009-0103-x Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.

Proper understanding of textual data requires the exploitation and integration of unstructured and heterogeneous clinical sources, healthcare records or scientific literature, which are fundamental aspects in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that enables the processing, classification and structuring of textual resources. In the past, several approaches for assessing word similarity by exploiting different knowledge sources (ontologies, thesauri, domain corpora, etc.) have been proposed. Some of these measures have been adapted to the biomedical field by incorporating domain information extracted from clinical data or from medical ontologies (such as MeSH or SNOMED CT). In this paper, these approaches are introduced and analyzed in order to determine their advantages and limitations with respect to the considered knowledge bases. After that, a new measure based on the exploitation of the taxonomical structure of a biomedical ontology is proposed. Using SNOMED CT as the input ontology, the accuracy of our proposal is evaluated and compared against other approaches according to a standard benchmark of manually ranked medical terms. The correlation between the results of the evaluated measures and the human experts’ ratings shows that our proposal outperforms most of the previous measures avoiding, at the same time, some of their limitations.

Introduction: The K4, Care European Project proposes a standard model for home-care (HC) assistance of elderly chronic patients (ECP), coordinating all professionals and institutions involved in HC for proper assistance. The model is supported by a distributed multi agent platform (MAP), a metaphor of the HC itself. Every person involved is represented by an intelligent agent interacting with other agents, representing other colleagues. Warnings are sent to the corresponding agent when required by other agents for some action to complete a medical service. Correct transferring of the HC knowledge to the MAP is a key issue. Methods: The actor profile ontology (APO) was build to determine the behaviour of MAP: the services to be provided have been specified by combinations of simpler actions; liabilities, responsibilities and access rights of every kind of professional involved in HC is specified, as well as interaction between different professionals. Agents are created according to APO definitions. Results: A prototype is implemented where physicians, nurses, social workers, carers... coordinate through their personal agents for HC of ECP, according to APO. The platform is currently being tested with real patients in Pollenza (It). Conclusion: Ontologies allow knowledge transmission to a computer application. APO provides high flexibility to mid and log-term evolution of HC, as changing it automatically changes the system behaviour. The basic structure of an ontology to support distributed medical complex systems is identified and this structure is easily exportable to other kind of medical systems, like hospital management or provision of dependency services.

Knowledge tailoring is a powerful tool to customize a system according to the particular view and interests of the user, in such a way that usability of the system becomes significantly improved. In this paper, the tailoring of an actor profile ontology is discussed in the context of a HomeCare integrated system that is being designed and implemented in the EU K4Care project. The paper analyzes different ways of implementing the automatic tailoring of an ontology. A tool called ATAPO, which has been designed and implemented to assist the user in this automatic tailoring is presented.

Computation of semantic similarity between concepts is a very common problem in many language related tasks and knowledge domains. In the biomedical field, several approaches have been developed to deal with this issue by exploiting the knowledge available in domain ontologies (SNOMED-CT) and specific, closed and reliable corpuses (clinical data). However, in recent years, the enormous growth of the Web has motivated researchers to start using it as the base corpus to assist semantic analysis of language. This paper proposes and evaluates the use of the Web as background corpus for measuring the similarity of biomedical concepts. Several classical similarity measures have been considered and tested, using a benchmark composed by biomedical terms and comparing the results against approaches in which specific clinical data were used. Results shows that the similarity values obtained from the Web are even more reliable than those obtained from specific clinical data, manifesting the suitability of the Web as an information corpus for the biomedical domain.

Healthcare applications involve complex structures of interacting processes and professionals that need to exchange information to provide the care services. In this kind of systems many different professional competencies, ethical and sensibility requirements as well a legal frameworks coexist and because of that the information managed inside the system should not be freely accessed, on the contrary it must be subject to very complex privacy restrictions. This is particularly critical in distributed systems, where additionally, security in remote transmissions must be ensured. In the paper we address the fundamental security issues that must be considered in design of a distributed healthcare application. The particular case of the K4CARE system is presented as an example of an application to the medical domain of homecare assistance. Homecare involves professionals from different institutions (hospital, social work organisms, etc) structurally independent, which must interact around any particular patient, and which use to be located in different physical places having their own and independent information systems.

The care of senior patients requires a great amount of human and sanitary resources. The K4Care Project is developing a new European model to improve the home care assistance of these patients. This medical model will be supported by an intelligent platform. This platform has two main layers: a multi-agent system and a knowledge layer. In this paper, it is reviewed the initial design of the system, and some improvements are presented. The main contribution is the introduction of an intermediate layer between agents and knowledge: the Data Abstraction Layer. Using this additional layer agents can have a transparent access to many different knowledge sources, which have data stored in different languages. In addition, the new layer would make possible to make intelligent treatment of the queries in order to generate answers in a more effective and efficient way.

One of the tasks towards the definition of a model for a home care system, is the definition of the different roles of the users. The roles indicate which actions and services perform each type of actor. In this paper we present an ontology for representing the nuclear home care actors and their associated information. To make it scalable and incremental, the ontology is prepared for including the knowledge of additional care units. To work with this model a tool called ISA has been developed. ISA allows the medical users to define new care units without the necessity of knowing the ontology structure.

Similarity measures are usually used to compare items and identify pairs or groups of similar individuals. The similarity measure strongly depends on the type of values to compare. We have faced the problem of considering that the information of the individuals is a sequence of events (i.e. sequences of web pages visited by a certain user or the personal daily schedule). Some measures for numerical sequences exist, but very few methods consider sequences of categorical data. In this paper, we present a new similarity measure for sequences of categorical labels and compare it with the previous approaches.

In this paper we describe an application called GruSMA1 which is an agent-based system that provides medical services to its users (the citizens or the visitors of a city). This multi-agent system contains agents that have information about the medical centres, departments and doctors of the city. All these agents coordinate their tasks to provide a set of services to the user of the system, who can search for medical centres satisfying a given set of requirements, as well as access his/her medical record, or make a booking to be visited by a particular kind of doctor. Special care has been paid to the definition of a medical ontology and to the implementation of mechanisms that guarantee the confidentiality in the access and transmission of medical data

In this paper, the problem of managing sewage sludge disposal on agricultural soils is addressed. Sewage sludge application on agricultural soils is recommended by governments in order to recycle nutrients and organic matter. However, this managing practice may lead to ecological and human risks. In this work, we propose the use of a multi-attribute preference approach to rate and rank a set of possible soils where some defined sewage sludge could be applied. The paper is focused on the assessment of the preference value functions. The variables involved in this problem are carefully explained and different preference assessment methods are proposed according to the different nature of the variables involved. In particular, the paper explains how fuzzy expert systems could be used for modelling the interactions between sludge and soil properties.

In this work we propose a new discretization method for quantitative domains. We give an overview of the methods in the literature and we introduce a new one that returns better results. As the need for a discretization method appeared in the definition of a procedure for evaluating the semantics of linguistic labels defined in a previous paper, we also give in this paper an overview of this semantics and of its analysis. ©1999 John Wiley & Sons, Inc.

In this paper, we propose a method to obtain the set of best alternatives, when data involved in MCDM are of different types (e.g. numbers, linguistic labels or truth values). This method consists of a classification of the alternatives according to criteria, and then an ordering of the classes obtained. In this way, we study each alternative in relation with the other ones. The groups formed indicate that their members are equally prefered over the others. Otherwise, we describe some specialized methods to be used in case that all criteria have the same type of values.

This paper studies why British tourists decide to travel to a particular destination in a Catalan region. The analysis is based on a survey that includes open-ended questions. First, we propose the operationalization of the concepts of motivation and meaning as push–pull factors when choosing a destination. Second, an ontology-based clustering method is presented, which makes it possible to analyse these qualitative factors from a semantic perspective to obtain tourist segments. A benchmark confirms that the segmentation obtained is better than that generated using classic clustering methods The results show that different meanings can be associated with any single place.