Vicomtech 4 Research Center

Donostia / San Sebastián, Spain

Vicomtech 4 Research Center

Donostia / San Sebastián, Spain
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Artetxe A.,Vicomtech 4 Research Center | Ayerdi B.,University of the Basque Country | Grana M.,University of the Basque Country | Rios S.,University of Chile
Journal of Computational Science | Year: 2017

This paper provides a real life application of the recently published Anticipative Hybrid Extreme Rotation Forest (AHERF), which is an heterogeneous ensemble classifier that anticipates the correct fraction of instances from each basic classifier architecture to be included in the ensemble. Heterogeneous classifier ensembles aim to profit from the diverse problem domain specificities of each classifier architecture in order to achieve improved generalization over a larger spectrum of problem domains. Given a problem dataset, anticipative determination of the desired ensemble composition is carried out as follows: First, we estimate the performance of each classifier architecture by independent pilot cross-validation experiments on a small subsample of the data. Next, classifier architectures are ranked according to their accuracy results. The likelihood of each classifier architecture instance appearing in the ensemble is computed from this ranking. Finally, while building the ensemble, the architecture of each individual classifier is decided by sampling this likelihood probability distribution. In this paper we provide an application of AHERF to a real life problem. Readmission of patients short time (i.e. 72. h) after being released poses a great economical and social challenge, so that many efforts are being addressed to predict and avoid readmission events. We present the results of the application of AHERF over a real life dataset composed of 156,120 admission cases recorded between January 2013 and August 2015. AHERF archives results over or close to 70% sensitivity in the prediction of readmissions for adults and pediatric cases, suggesting that it can be used to build institution specific prediction systems. © 2017 Elsevier B.V.

Artetxe A.,Vicomtech 4 Research Center | Artetxe A.,San Sebastián University | Beristain A.,Vicomtech 4 Research Center | Beristain A.,San Sebastián University | And 2 more authors.
Advances in Intelligent Systems and Computing | Year: 2017

Objective: Predicting Emergency Department (ED) readmissions is of great importance since it helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. It is becoming standard procedure to evaluate the risk of ED readmission within 30 days after discharge. Methods. Our dataset is stratified into four groups according to the Kaiser Permanente Risk Stratification Model. We deal with imbalanced data using different approaches for resampling. Feature selection is also addressed by a wrapper method which evaluates feature set importance by the performance of various classifiers trained on them. Results. We trained a model for each scenario and subpopulation, namely case management (CM), heart failure (HF), chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM). Using the full dataset we found that the best sensitivity is achieved by SVM using over-sampling methods (40.62 % sensitivity, 78.71 % specificity and 71.94 accuracy). Conclusions. Imbalance correction techniques allow to achieve better sensitivity performance, however the dataset has not enough positive cases, hindering the achievement of better prediction ability. The arbitrary definition of a threshold-based discretization for measurements which are inherently is an important drawback for the exploitation of the data, therefore a regression approach is considered as future work. © Springer International Publishing AG 2017.

Artetxe A.,Vicomtech 4 Research Center | Artetxe A.,San Sebastián University | Grana M.,San Sebastián University | Beristain A.,Vicomtech 4 Research Center | Rios S.,University of Chile
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

Short time readmission prediction in Emergency Departments (ED) is a valuable tool to improve both the ED management and the healthcare quality. It helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. As in many other medical domains, patient readmission data is heavily imbalanced, i.e. the minority class is very infrequent, which is a challenge for the construction of accurate predictors using machine learning tools. We have carried computational experiments on a dataset composed of ED admission records spanning more than 100000 patients in 3 years, with a highly imbalanced distribution. We employed various approaches for dealing with this highly imbalanced dataset in combination with different classification algorithms and compared their predictive power for the estimation of the ED readmission probability within 72 h after discharge. Results show that random undersampling and Bagging (RUSBagging) in combination with Random Forest achieves the best results in terms of Area Under ROC Curve (AUC). © Springer International Publishing AG 2017.

Artetxe A.,Vicomtech 4 Research Center | Artetxe A.,San Sebastián University | Larburu N.,Vicomtech 4 Research Center | Murga N.,Hospital Universitario Of Basurto Osakidetza Health Care System | And 2 more authors.
Smart Innovation, Systems and Technologies | Year: 2018

Heart Failure (HF) is a clinical syndrome caused by a structural and/or functional cardiac abnormality that imposes tremendous burden on patients and on the healthcare systems worldwide. In this context, predictive models may facilitate the identification of patients at high risk of death or unplanned hospital readmissions and potentially enable direct specific interventions. Currently a plethora of studies in this field is discussing whether hospital readmission and mortality can be effectively predicted in patients with HF. In this work, we present a preliminary study for identifying risk factors for unplanned readmission or death, using a clinical dataset with 119 patients and 60 features. Different classification algorithms and feature selection approaches were employed in order to increase the prediction ability of the models and reduce their complexity in terms of number of features. Results show that sequential feature selection methods along with SVM achieve the best scores in terms of accuracy for predicting 30-day readmission or death risk. © Springer International Publishing AG 2018.

Szczerbicki E.,University of Newcastle | Grana M.,University of the Basque Country | Posada J.,Vicomtech 4 Research Center | Toro C.,Vicomtech 4 Research Center
Cybernetics and Systems | Year: 2013

The special edition of Cybernetics and Systems contains selected and reviewed papers that expand significantly on topics covered by some conferences on knowledge engineering and smart knowledge-based systems design and development. The selection enhances efforts toward intelligent systemic approaches researched in this area of new, innovative, and significant developments that can be of great interest to cybernetics and systems science professionals. The authors of the paper titled 'Impact of Reflexive Ontologies in Semantic Clinical Decision Support Systems' introduce reflex ontologies (ROs) as a novel approach intended to reduce time consumption problems while providing as a result a fast reaction from ontology-based applications. The presented real-life application is a case study for a knowledge-based clinical decision support system for the diagnosis of Alzheimer's disease.

Barrena N.,Vicomtech 4 Research Center | Navarro A.,Vicomtech 4 Research Center | Oyarzun D.,Vicomtech 4 Research Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

This paper describes the results of a R&D project focused on the creation of a flexible and easy-to-use platform for creating advanced multimedia applications, with strong focus on education and leisure contexts. Most of the similar existing platforms lack of enough flexibility to create different kind of content, or they require a strong technical background for authoring applications. In this project, a balance between flexibility and easiness has been fulfilled. Several use cases, whose authors have different levels of technical background, have been defined to validate it. © Springer International Publishing Switzerland 2016.

Diez H.V.,Vicomtech 4 Research Center | Segura A.,Vicomtech 4 Research Center | Garcia-Alonso A.,Vicomtech 4 Research Center | Oyarzun D.,Vicomtech 4 Research Center
Multimedia Tools and Applications | Year: 2016

This paper contributes to the efficient visualization and management of 3D content for e-commerce purposes. The main objective of this research is to improve the multimedia management of complex 3D models, such as CAD or BIM models, by simply dragging a CAD/BIM file into a web application. Our developments and tests show that it is possible to convert these models into web compatible formats. The platform we present performs this task requiring no extra intervention from the user. This process makes sharing 3D content on the web immediate and simple, offering users an easy way to create rich accessible multiplatform catalogues. Furthermore, the platform enables users to view and interact with the uploaded models on any WebGL compatible browser favouring collaborative environments. Despite not being the main objective of this work, an interface with search engines has also been designed and tested. It shows that users can easily search for 3D products in a catalogue. The platform stores metadata of the models and uses it to narrow the search queries. Therefore, more precise results are obtained. © 2016 Springer Science+Business Media New York

Garcia-Pablos A.,Vicomtech 4 Research Center | Cuadros M.,Vicomtech 4 Research Center | Rigau G.,IXA Group
Procesamiento de Lenguaje Natural | Year: 2015

Sentiment analysis is the area of Natural Language Processing that aims to determine the polarity (positive, negative, neutral) contained in an opinionated text. A usual resource employed in many of these approaches are the so-called polarity lexicons. A polarity lexicon acts as a dictionary that assigns a sentiment polarity value to words. In this work we explore the possibility of automatically generating domain adapted polarity lexicons employing continuous word representations, in particular the popular tool Word2Vec. First we show a qualitative evaluation of a small set of words, and then we show our results in the SemEval-2015 task 12 using the presented method. © 2015 Sociedad Española para el Procesamiento del Lenguaje Natural.

Toro C.,Vicomtech 4 Research Center | Barandiaran I.,Vicomtech 4 Research Center | Posada J.,Vicomtech 4 Research Center
Procedia Computer Science | Year: 2015

A worldwide trend in advanced manufacturing countries is defining Industrie 4.0, Industrial Internet and Factories of the Future as a new wave that can revolutionize the production and its associated services. Cyber-Physical Systems (CPS) are central to this vision and are entitled to be part of smart machines, storage systems and production facilities able to exchange information with autonomy and intelligence. Such systems should be able to decide and trigger actions, and control each other independently and for such reason it is required the use of Knowledge based and intelligent information approaches. In this paper we present our perspective on how to support Industrie 4.0 with Knowledge based and intelligent systems. We focus in the conceptual model, architecture and necessary elements we believe are required for a real world implementation. We base our conceptualization in the experiences gathered during the participation in different ongoing research projects where the presented architecture is being implemented. © 2015 The Authors. Published by Elsevier B.V.

Diez H.V.,Vicomtech 4 Research Center | Garcia S.,Vicomtech 4 Research Center | Sanchez J.R.,Vicomtech 4 Research Center | Del Puy Carretero M.,Vicomtech 4 Research Center
Proceedings - Web3D 2013: 18th International Conference on 3D Web Technology | Year: 2013

The goal of the work presented in this paper is to develop a 3D web based online tutoring system that enhances the motivation and cognitive development of students. To achieve this, a virtual assistant will be integrated to the e-learning platform; this 3D modeled e-tutor will evaluate each student individually, it will react to their learning progress by empathetic gestures and it will guide them through the lectures according to their personal needs. The accomplishment of these tasks will imply a thorough study of the latest techniques on artificial intelligence, multi-agent architectures and their representation by means of 3D emotional avatars. Copyright © ACM 978-1-4503-2133-4/13/06 $15.00.

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