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1 to 50 of 387 results
  • A data mining approach to identify cognitive NeuroRehabilitation Range in Traumatic Brain Injury patients

     García Rudolph, Alejandro; Gibert Oliveras, Karina
    Expert systems with applications
    Date of publication: 2014-09-01
    Journal article

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    Cognitive rehabilitation (CR) treatment consists of hierarchically organized tasks that require repetitive use of impaired cognitive functions in a progressively more demanding sequence. Active monitoring of the progress of the subjects is therefore required, and the difficulty of the tasks must be progressively increased, always pushing the subjects to reach a goal just beyond what they can attain. There is an important lack of well-established criteria by which to identify the right tasks to propose to the patient. In this paper, the NeuroRehabilitation Range (NRR) is introduced as a means of identifying formal operational models. These are to provide the therapist with dynamic decision support information for assigning the most appropriate CR plan to each patient. Data mining techniques are used to build data-driven models for NRR. The Sectorized and Annotated Plane (SAP) is proposed as a visual tool by which to identify NRR, and two data-driven methods to build the SAP are introduced and compared. Application to a specific representative cognitive task is presented. The results obtained suggest that the current clinical hypothesis about NRR might be reconsidered. Prior knowledge in the area is taken into account to introduce the number of task executions and task performance into NRR models and a new model is proposed which outperforms the current clinical hypothesis. The NRR is introduced as a key concept to provide an operational model identifying when a patient is experiencing activities in his or her Zone of Proximal Development and, consequently, experiencing maximum improvement. For the first time, data collected through a CR platform has been used to find a model for the NRR. © 2014 Elsevier Ltd. All rights reserved.

  • Mixed intelligent-multivariate missing imputation

     Gibert Oliveras, Karina
    International journal of computer mathematics
    Date of publication: 2014-01-02
    Journal article

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    In real applications, important rates of missing data are often found and have to be pre-processed before the analysis. The literature for missing imputation is abundant. However, the most precise imputation methods require long time, and sometimes specic software; this implies a signicant delay to get nal results. The Mixed Intelligent-Multivariate Missing Im- putation (MIMMI) method is proposed as a hybrid missing imputation methodology based on clustering. MIMMI is a non parametric method that combines the prior expert knowledge with multivariate analysis without requiring assumptions on the probabilistic models of the variables (normality, exponentiality, etc). The proposed imputation values implicitly take into account the joint distribution of all variables and can be determined in a relatively short time. MIMMI uses the conditional mean according to the self-underlying structure of the dataset. It provides a good trade-o between accuracy and both simplicity and required time to data preparation. The mechanics of the method is illustrated with some case-studies, both synthetic and real applications related with human behavior. In both cases, acceptable quality results were obtained in short time.

  • Big Data and real-time decision-making applied to food and beverage online industry (BigFood)

     Haro Ramos, Jesús; Gibert Oliveras, Karina
    Participation in a competitive project

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  • Annotated-Traffic Lights Panel as a Tool to Evaluate the Risk of Decisions Based on Prototypes

     Conti, Dante; Gibert Oliveras, Karina
    Frontiers in artificial intelligence and applications
    Date of publication: 2013-10-01
    Journal article

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    Clustering techniques are commonly performed to find homogeneous and distinguishable prototypes. However, a careful interpretation of these prototypes is the key to assist the expert to better organize this knowledge for decision making support. We use the annotated Traffic Lights Panel (aTPL), not only as a postprocessing tool to help understand clustering prototypes, but also to manage uncertainty related to variability which is inherent by itself within the prototypes. The aTPL handles this uncertainty by using the variation coefficients (VC) in the classes over two dimensions - tone and saturation. Two different aTLPs were obtained to a WWTP in Slovenia. Results suggested that aTPL could be seen as a useful tool with good levels of reliability when interpreting and managing uncertainty related to decision-making based on clustering prototypes.

  • Incorporating local information and prior expert knowledge to evidence-informed mental health system research

     Salvador Carulla, Luis; García Alonso, Carlos; Gibert Oliveras, Karina; Vázquez Bourgon, Javier
    Date of publication: 2013-03-10
    Book chapter

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  • Preface

     Gibert Oliveras, Karina; Botti Navarro, Vicent; Reig Bolano, Ramon
    Date of publication: 2013-10-01
    Book chapter

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  • 1st Hacking Bullipedia Global Challenge

     Gibert Oliveras, Karina; Vazquez Salceda, Javier; Alvarez Napagao, Sergio; Oliva Felipe, Luis Javier; Sevilla Villanueva, Beatriz; Tejeda Gomez, Jose Arturo; Gomez Sebastia, Ignasi
    Award or recognition

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  • First Hackingbullipedia Global Challenge

     Gibert Oliveras, Karina; Vazquez Salceda, Javier
    Award or recognition

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  • Finding patterns in cognitive rehabilitation

     García Rudolph, Alejandro; Gibert Oliveras, Karina
    Frontiers in artificial intelligence and applications
    Date of publication: 2013-10
    Journal article

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    Traumatic brain injury (TBI) is the leading cause of death and disability in children and young adults around the world. There is not enough on-field experience yet regarding which specific intervention (tasks or exercises assignation) is more appropriated to help therapists to design their cognitive rehabilitation (CR) plans. Our proposal is to consider the CR treatment as a sequence of tasks and to determine the associations between a CR treatment (or relevant subsequences of it) and the degree of response of the patient to it. In the proposed methodology, a clustering process is performed in such a way that treatment profiles (classes) are identified.. Afterwards, responses to CR (improvements) of the patients placed in the different classes have been studied by means of conditional distributions of variables versus the classes. Analyzing CR tasks as treatment patterns offers a different perspective from the traditional single task focus, and may provide a comprehensive approach to therapists to design CR programs.

  • Access to the full text
    Post-processing the Class Panel Graphs: towards understandable patterns from data  Open access

     Sevilla Villanueva, Beatriz; Gibert Oliveras, Karina; Sànchez Marrè, Miquel
    Frontiers in artificial intelligence and applications
    Date of publication: 2013-11-01
    Journal article

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    A profiling methodology is introduced for automatic interpretation of clusters in this work. This methodology contributes to the characterization of the resulting classes from a clustering process. This work aims to find a concordance between the proposed methodology and the experts¿ description of these classes. In this work the resulting classes from a clustering of a general population sample based on their diet and physical activity habits are interpreted and compared with the experts¿ description of these classes by using the Class Panel Graphs. In this work, we import techniques from the multivariate analysis into the cluster interpretation process.

    A profiling methodology is introduced for automatic interpretation of clusters in this work. This methodology contributes to the characterization of the resulting classes from a clustering process. This work aims to find a concordance between the proposed methodology and the experts’ description of these classes. In this work the resulting classes from a clustering of a general population sample based on their diet and physical activity habits are interpreted and compared with the experts’ description of these classes by using the Class Panel Graphs. In this work, we import techniques from the multivariate analysis into the cluster interpretation process.

    Postprint (author’s final draft)

  • Editorial of Artificial Intelligence Research and Development [Proceedings of the 16th International Conference (CCIA 2013), held at the University of Vic (UVIC), Catalonia, Spain, in October 2013]

     Sevilla Villanueva, Beatriz; Gibert Oliveras, Karina; Botti Navarro, Vicent; Reig Bolaño, Ramón
    Frontiers in artificial intelligence and applications
    Date of publication: 2013-10-01
    Journal article

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    For almost twenty years the Catalan Association of Artificial Intelligence (ACIA) has been promoting cooperation between researchers in artificial intelligence within the Catalan speaking community. This book presents the proceedings of the 16th International Conference (CCIA 2013), held at the University of Vic (UVIC), Catalonia, Spain, in October 2013. This annual conference aims to foster discussion of the latest developments in artificial intelligence within the community of Catalan countries, as well as amongst members of the AI community worldwide. The book contains the 26 full papers, 5 short papers and 12 poster presentations from the conference, which are grouped under the following topics: relational learning, planning; satisfiability and constraints; perception and image processing; preprocessing; patterns extraction and learning; post-processing, model interpretability and decision support; recommenders, similarity and CBR; and multiagent systems.

  • Introducing semantic variables in mixed distance measures. Impact on hierarchical clustering

     Gibert Oliveras, Karina; Valls Mateu, Aïda; Batet Sanromà, Montserrat
    Knowledge and information systems
    Date of publication: 2013-05
    Journal article

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  • Access to the full text
    Clustering and interpretation on real nutritional data  Open access

     Sevilla Villanueva, Beatriz; Gibert Oliveras, Karina; Sànchez Marrè, Miquel
    Conferencia de la Asociación Española para la Inteligencia Artificial
    Presentation's date: 2013-09-18
    Presentation of work at congresses

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    Nutritional Genomics studies diet-gene-disease interactions and aims to promote health and disease prevention. It is based on the idea that everything ingested into a person¿s body affects the genome of the individual and, therefore, both genes and nutrients modify the same metabolic processes. This paper presents an application of clustering and interpretation over real heterogeneous data coming from a nutritional study. The individuals are clustered by their diet and physical activity habits and the resulting clustering is interpreted. This work is part of a methodology to deal with data from dietary intervention studies.

    Nutritional Genomics studies diet-gene-disease interactions and aims to promote health and disease prevention. It is based on the idea that everything ingested into a person’s body affects the genome of the individual and, therefore, both genes and nutrients modify the same metabolic processes. This paper presents an application of clustering and interpretation over real heterogeneous data coming from a nutritional study. The individuals are clustered by their diet and physical activity habits and the resulting clustering is interpreted. This work is part of a methodology to deal with data from dietary intervention studies.

  • Data Mining and post-processing tools to extract comprehensible patterns from Venezuelan Financial Assets

     Conti, Dante; Gibert Oliveras, Karina
    Mathematical Modelling in Engineering & Human Behaviour
    Presentation's date: 2013-09
    Presentation of work at congresses

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  • Semantic similarity estimation from multiple ontologies

     Batet, Montserrat; Sánchez, David; Valls Mateu, Aïda; Gibert Oliveras, Karina
    Applied intelligence
    Date of publication: 2012-05-26
    Journal article

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  • Assisting the end-user in the interpretation of profiles for decision support. An application to wastewater treatment plants

     Gibert Oliveras, Karina; Conti, Dante; Vrecko, D.
    Environmental Engineering and Management Journal
    Date of publication: 2012
    Journal article

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  • Use of functioning-disability and dependency for case-mix and subtyping of schizophrenia

     Ochoa, Susana; Salvador Carulla, Luis; Villalta Gil, Victoria; Gibert Oliveras, Karina; Haro, Josep Maria
    European journal of psychiatry
    Date of publication: 2012-03
    Journal article

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  • Decreasing uncertainty when interpreting profiles through the traffic lights panel

     Gibert Oliveras, Karina; Conti, Dante; Sànchez Marrè, Miquel
    Communications in computer and information science
    Date of publication: 2012
    Journal article

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  • Red Temática Española para el Avance y la Transferencia de la Inteligencia Computacional Aplicada

     Cortes Garcia, Claudio Ulises; Gibert Oliveras, Karina; Sànchez Marrè, Miquel
    Participation in a competitive project

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  • Tools for environmental data mining and intelligent decision support

     Gibert Oliveras, Karina; Sànchez Marrè, Miquel; Sevilla Villanueva, Beatriz
    International Congress on Environmental Modelling and Software
    Presentation's date: 2012-07-01
    Presentation of work at congresses

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  • A methodology for the characterization of intelligent environmental decision support systems

     Sànchez Marrè, Miquel; Gibert Oliveras, Karina; Cabello Jiménez, Alejandro; Sem, Federico
    International Congress on Environmental Modelling and Software
    Presentation's date: 2012-07-02
    Presentation of work at congresses

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  • A picture on environmental data mining real applications. What is done and how?

     Sànchez Marrè, Miquel; Gibert Oliveras, Karina
    International Congress on Environmental Modelling and Software
    Presentation's date: 2012-07-03
    Presentation of work at congresses

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    In this work a proposal for making systematic state of the art is presented and applied to the Environmental Data Mining field. The main characteristics of the Data Mining process have been identified. A form has been created to check which of those characteristics take place in a real application and how. A random sample of Science Citation Index papers regarding Data Mining and Environmental Applications has been selected. Papers were read by a set of experts and a form was filled in for every paper. The resulting information was mined itself using basic statistical analysis and some specific treatments for multi-response variables, to get a first picture of what is currently being done in the applications of Data Mining methods to environmental fields. Very interesting results have been obtained which depict very useful information. This information ranges from a general picture of what kind of methods are commonly used to which environmental areas seems to be more deeply using the data mining techniques. The paper presents and discuss these results, together with a proposal for building a continuous collaborative pannel in the web for enlarging the sample of papers and update the picture continuously. This will be easily possible because the analysis of the recorded data has been automatized in a statistical package set of macros for repetitive updating mined knowledge. The proposal is oriented to provide an Environmental Data Mining Observatoire, where getting updated information on what is being done in the area, identify drawbacks, orient future research in the methodological field to provide answer to the open environmental problems and finally, to give the environmental audience a wide corpus of previous experiences to be used as a reference for new applications

  • The use of traffic lights panel as a goodness-of-clustering indicator: an application to financial assets

     Conti, Dante; Gibert Oliveras, Karina
    Congrés Internacional de l¿Associació Catalana d'Intel·ligència Artificial
    Presentation's date: 2012-10-24
    Presentation of work at congresses

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  • A multivariate missing data imputation method based on clustering. Application to World Health Organization data

     Gibert Oliveras, Karina; Salvador-Carulla, L.; Morris, J.; Saxena, S.
    Modelling in Engineering and Human Behaviour
    Presentation's date: 2012-09
    Presentation of work at congresses

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  • Using AI in nutritional genomics

     Gibert Oliveras, Karina; Sànchez Marrè, Miquel; Sevilla Villanueva, Beatriz
    Seminario Doctoral en Aplicaciones y Transferencia de la Inteligencia Computacional
    Presentation's date: 2012-11
    Presentation of work at congresses

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  • Metodologia d'inducció i interpretació de Perfils Dinàmics I2DPro

     Rodríguez Silva, Gustavo; Gibert Oliveras, Karina
    Nodes: el butlletí de l'ACIA
    Date of publication: 2011
    Journal article

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  • Post-processing: bridging the gap between modelling and effective decision-support. The Profile Assessment Grid in Human Behaviour

     Gibert Oliveras, Karina; Rodríguez Silva, Gustavo; Annicchiarico, Roberta
    Mathematical and computer modelling
    Date of publication: 2011-11-28
    Journal article

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    The importance of post-processing the results of clustering when using data mining to support subsequent decision-making is discussed. Both the formal embedded binary logistic regression (EBLR) and the visual profile’s assessment grid (PAG) methods are presented as bridging tools for the real use of clustering results. EBLR is a sequence of logistic regressions that helps to predict the class of a new object; while PAG is a graphical tool that visualises the results of an EBLR. PAG interactively determines the most suitable class for a new object and enables subsequent follow-ups. PAG makes the underlying mathematical model (EBLR) more understandable, improves usability and contributes to bridging the gap between modelling and decision-support. When applied to medical problems, these tools can perform as diagnostic-support tools, provided that the predefined set of profiles refer to different stages of a certain disease or different types of patients with a same medical problem, etc. Being a graphical tool, PAG enables doctors to quickly and friendly determine the profile of a patient in the everyday activity, without necessarily understanding the statistical models involved in the process, which used to be a serious limitation for wider application of these methods in clinical praxis. In this work, an application is presented with 4 functional disability profiles.

  • Outcomes from the iEMSs data mining in the environmental sciences workshop series

     Gibert Oliveras, Karina; Sànchez Marrè, Miquel
    Environmental modelling & software
    Date of publication: 2011-07
    Journal article

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  • The strategy of combining antidepressants in the treatment of major depression: clinical experience in spanish outpatients

     Martín López, Luís M.; Gibert Oliveras, Karina; Rojo, José E.; Martín, Juan Carlos; Sperry, Lyli; Duñó, Lurdes; Balbuena, Antonio; Vallejo, Julio
    Depression Research and Treatment
    Date of publication: 2011-06-15
    Journal article

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  • Knowledge-driven delivery of home care services

     Batet, Montserrat; Isern, David; Marín, Lucas; Martínez, Sergio; Moreno, Antonio; Sánchez, David; Valls Mateu, Aïda; Gibert Oliveras, Karina
    Journal of intelligent information systems
    Date of publication: 2011-12-06
    Journal article

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    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.

  • Retrat d'un sector en moviment: estat de la biotecnologia, la biomedicina i les tecnologies mèdiques a Catalunya

     Alonso-Rodríguez, Nerea; Austin, Martin; Marca, Joan; Munné, Ricard; Parente, Antonio; Condom, Pere; Costas, Ignasi; Farré, Adela; Lurigados, Carlos; Martí, Jordi; Ouro, Albert; Princep, Marta; Vendrell, Montserrat; Gibert Oliveras, Karina; Martín Sánchez, Joan Carles; Twose, Angela
    Date of publication: 2011-10-26
    Book

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  • Ontology based semantic clustering

     Batet Sanromà, Montserrat
    Defense's date: 2011-02-15
    Universitat Rovira i Virgili
    Theses

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  • SUstainable and PERsuasive Human Users moBility in future cities

     Alvarez Napagao, Sergio; Gomez Sebastia, Ignasi; Oliva Felipe, Luis Javier; Tejeda Gomez, Jose Arturo; Gibert Oliveras, Karina; Sànchez Marrè, Miquel; Garcia Gasulla, Dario; Vazquez Salceda, Javier
    Participation in a competitive project

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  • A multivariate missing data inputation method based on clustering: application to WHO data

     Gibert Oliveras, Karina
    Special Meeting of Multivariate Analysis and Classification working group
    Presentation's date: 2011-05
    Presentation of work at congresses

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  • Access to the full text
    Semantic clustering based on ontologies: an application to the study of visitors in a natural reserve  Open access

     Batet, Montserrat; Valls Mateu, Aïda; Gibert Oliveras, Karina
    International conference on agents and artificial intelligence
    Presentation's date: 2011
    Presentation of work at congresses

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    The development of large ontologies for general and specific domains provides new tools to improve the quality of data mining techniques such as clustering. In this paper we explain how to improve clustering results by exploiting the semantics of categorical data by means of ontologies and how this semantics can be included into a hierarchical clustering method. We want to prove that when the conceptual meaning of the values is taken into account, it is possible to find a better interpretation of the clusters. This is demonstrated with the analysis of real data collected from visitors to of a Natural Reserve. The results of our methodology are compared with the ones obtained with a classical multivariate analysis done in the same database.

  • The benefits of hybrid methodologies in data mining and knowledge discovery: applications to human behaviour

     Gibert Oliveras, Karina
    Modelling in Engineering and Human Behaviour
    Presentation's date: 2011-09
    Presentation of work at congresses

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  • Effects of using mean curve in the analysis of repeated time series

     Gibert Oliveras, Karina; Rojo, Emili; Rodas, Jorge
    Acta informatica medica
    Date of publication: 2010
    Journal article

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    Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support  Open access

     Gibert Oliveras, Karina; García Alonso, Carlos; Salvador Carulla, Luis
    Health research policy and systems
    Date of publication: 2010-10-16
    Journal article

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    Background: Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. Method: This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge(IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case “1” and kappa in both cases. Results: EbCA is a new methodology composed by 6 steps: 1) Data collection and data preparation; 2) acquisition of “Prior Expert Knowledge” (PEK) and design of the “Prior Knowledge Base” (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. Discussion: This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.

  • Definición de "dependencia funcional": implicaciones para la política sociosanitaria

     Salvador Carulla, Luis; Gibert Oliveras, Karina; Ochoa, Susana
    Atención primaria
    Date of publication: 2010-03-04
    Journal article

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  • Semantic clustering using multiple ontologies

     Batet, Montserrat; Valls Mateu, Aïda; Gibert Oliveras, Karina; Sánchez, David
    Frontiers in artificial intelligence and applications
    Date of publication: 2010-10
    Journal article

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  • A preliminary taxonomy and a standard knowledge base for mental-health system indicators in Spain

     Salvador Carulla, Luis; Salinas-Pérez, José Alberto; Martín, Manuel; Grané, Mont-serrat; Gibert Oliveras, Karina; Roca, Miquel; Balbuena, Antonio
    International journal of mental health systems
    Date of publication: 2010-11
    Journal article

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  • Knowledge discovery with clustering based on rules by states: a water treatment application

     Gibert Oliveras, Karina; Rodríguez Silva, Gustavo; Rodríguez Roda, Ignasi
    Environmental modelling & software
    Date of publication: 2010-06
    Journal article

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    This work presents advances in the design of a hybrid methodology that combines artificial intelligence and statistical tools to induce a model of explicit knowledge in relation to the dynamics of a wastewater treatment plant. The methodology contributes to problem solving under the paradigm of knowledge discovery from data in which the pre-process, the automatic interpretation of results and the explicit production of knowledge play a role as important as the analysis itself. The data mining step is performed using clustering based on rules by states, which integrates the knowledge discovered separately at each step of the process into a single model of global operation of the phenomenon. This provides a more accurate model for the dynamics of the system than one obtained by analyzing the whole dataset with all the steps taken together.

  • Using ontologies for structuring organizational knowledge in Home Care assistance

     Valls Mateu, Aïda; Gibert Oliveras, Karina; Sánchez, David; Batet, Montserrat
    International journal of medical informatics
    Date of publication: 2010-05
    Journal article

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    Purpose: Information Technologies and Knowledge-based Systems can significantly improve the management of complex distributed health systems, where supporting multidisciplinarity is crucial and communication and synchronization between the different professionals and tasks becomes essential. This work proposes the use of the ontological paradigm to describe the organizational knowledge of such complex healthcare institutions as a basis to support their management. The ontology engineering process is detailed, as well as the way to maintain the ontology updated in front of changes. The paper also analyzes how such an ontology can be exploited in a real healthcare application and the role of the ontology in the customization of the system. The particular case of senior Home Care assistance is addressed, as this is a highly distributed field as well as a strategic goal in an ageing Europe. Materials and methods: The proposed ontology design is based on a Home Care medical model defined by an European consortium of Home Care professionals, framed in the scope of the K4Care European project (FP6). Due to the complexity of the model and the knowledge gap existing between the – textual – medical model and the strict formalization of an ontology, an ontology engineering methodology (On-To-Knowledge) has been followed. Results: After applying the On-To-Knowledge steps, the following results were obtained: the feasibility study concluded that the ontological paradigm and the expressiveness of modern ontology languages were enough to describe the required medical knowledge; after the kick-off and refinement stages, a complete and non-ambiguous definition of the Home Care model, including its main components and interrelations, was obtained; the formalization stage expressedHCmedical entities in the form of ontological classes, which are interrelated by means of hierarchies, properties and semantically rich class restrictions; the evaluation, carried out by exploiting the ontology into a knowledge-driven e-health application running on a real scenario, showed that the ontology design and its exploitation brought several benefits with regards to flexibility, adaptability and work efficiency from the end-user point of view; for the maintenance stage, two software tools are presented, aimed to address the incorporation and modification of healthcare units and the personalization of ontological profiles.Conclusions: The paper shows that the ontological paradigm and the expressiveness of modern ontology languages can be exploited not only to represent terminology in a nonambiguous way, but also to formalize the interrelations and organizational structures involved in a real and distributed healthcare environment. This kind of ontologies facilitates the adaptation in front of changes in the healthcare organization or Care Units, supports the creation of profile-based interaction models in a transparent and seamless way, and increases the reusability and generality of the developed software components. As a conclusion of the exploitation of the developed ontology in a real medical scenario, we can say that an ontology formalizing organizational interrelations is a key component for building effective distributed knowledge-driven e-health systems.

  • Inference of lexical ontologies. The LeOnI methodology

     Farreres De La Morena, Javier; Gibert Oliveras, Karina; Rodriguez Hontoria, Horacio; Pluempitiwiriyawej, Charnyote
    Artificial intelligence
    Date of publication: 2010-01
    Journal article

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    In this article we present a method for semi-automatically deriving lexico-conceptual ontologies in other languages, given a lexico-conceptual ontology for one language and bilingual mapping resources. Our method uses a logistic regression model to combine mappings proposed by a set of classifiers (up to 17 in our implementation). The method is formally described and evaluated by means of two implementations for semi-automatically building Spanish and Thai WordNets using Princeton's WordNet for English and conventional English¿Spanish and English¿Thai bilingual dictionaries.

  • Performance of ontology-based semantic similarities in clustering

     Batet, Montserrat; Valls Mateu, Aïda; Gibert Oliveras, Karina
    Date of publication: 2010-06
    Book chapter

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  • Privacy preserving and use of medical information in a multiagent system

     Gibert Oliveras, Karina; Valls Mateu, Aïda; Lhotska, Lenka; Aubrecht, Petr
    Date of publication: 2010
    Book chapter

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    The design of cryptographic and security protocols for new scenarios and applications can be computationally expensive. Examples of these can be sensor or mobile ad-hoc networks where thousands of nodes can be involved. In such cases, the aid of an automated tool generating protocols for a predefined problem can be of great utility. This work uses the genetic algorithms (GA) techniques for the automatic design of security networked protocols. When using GA for optimizing protocols two aspects are critical: the genome definition and the evaluation function. We discuss how security protocols can be represented as binary strings and can be interpreted as security protocols; moreover we define several basic criteria for evaluating security protocols. Finally, we present the software we developed for generating secure communications protocols and show some examples and obtained results.

  • Exploiting taxonomical knowledge to compute semantic similarity: an evaluation in the biomedical domain

     Batet, Montserrat; Sánchez, David; Valls Mateu, Aïda; Gibert Oliveras, Karina
    Date of publication: 2010-07
    Book chapter

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  • Desarrollo de un conjunto b

     Gibert Oliveras, Karina; Salvador Carulla, Luis
    Participation in a competitive project

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  • Performance of ontology-based semantic similarities in clustering

     Batet, Montserrat; Valls Mateu, Aïda; Gibert Oliveras, Karina
    International Conference on Artificial Intelligence and Soft Computing
    Presentation's date: 2010-06
    Presentation of work at congresses

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