Romero Merino, Enrique
Total activity: 81
Research group
SOCO - Soft Computing
Department
Department of Computer Science
School
Barcelona School of Informatics (FIB)
E-mail
eromerocs.upc.edu
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1 to 50 of 81 results
  • Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks

     Arizmendi Pereira, Carlos Julio; Sierra Bueno, Daniel Alfonso; Vellido Alcacena, Alfredo; Romero Merino, Enrique
    Expert systems with applications
    Vol. 41, num. 11, p. 5296-5307
    DOI: 10.1016/j.eswa.2014.02.031
    Date of publication: 2014-09-01
    Journal article

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    Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, which are the source of valuable data in the form of signals and images. Expert radiologists should benefit from their use as part of an at least partially automated computer-based medical decision support system. This paper focuses on Magnetic Resonance Spectroscopy signal analysis and illustrates a method that combines Gaussian Decomposition, dimensionality reduction by Moving Window with Variance Analysis and classification using adaptively regularized Artificial Neural Networks. The method yields encouraging results in the task of binary classification of human brain tumours, even for tumour types that have seldom been analyzed from this viewpoint. © 2014 Elsevier Ltd. All rights reserved.

  • Sepsis mortality prediction with the Quotient Basis Kernel

     Ribas Ripoll, Vicent; Vellido Alcacena, Alfredo; Romero Merino, Enrique; Ruiz Rodriguez, Juan Carlos
    Artificial intelligence in medicine
    Vol. 61, num. 1, p. 45-52
    DOI: 10.1016/j.artmed.2014.03.004
    Date of publication: 2014-05-01
    Journal article

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    Objective: This paper presents an algorithm to assess the risk of death in patients with sepsis. Sepsis is a common clinical syndrome in the intensive care unit (ICU) that can lead to severe sepsis, a severe state of septic shock or multi-organ failure. The proposed algorithm may be implemented as part of a clinical decision support system that can be used in combination with the scores deployed in the ICU to improve the accuracy, sensitivity and specificity of mortality prediction for patients with sepsis. Methodology: In this paper, we used the Simplified Acute Physiology Score (SAPS) for ICU patients and the Sequential Organ Failure Assessment (SOFA) to build our kernels and algorithms. In the proposed method, we embed the available data in a suitable feature space and use algorithms based on linear algebra, geometry and statistics for inference. We present a simplified version of the Fisher kernel (practical Fisher kernel for multinomial distributions), as well as a novel kernel that we named the Quotient Basis Kernel (QBK). These kernels are used as the basis for mortality prediction using soft-margin support vector machines. The two new kernels presented are compared against other generative kernels based on the Jensen-Shannon metric (centred, exponential and inverse) and other widely used kernels (linear, polynomial and Gaussian). Clinical relevance is also evaluated by comparing these results with logistic regression and the standard clinical prediction method based on the initial SAPS score. Results: As described in this paper, we tested the new methods via cross-validation with a cohort of 400 test patients. The results obtained using our methods compare favourably with those obtained using alternative kernels (80.18% accuracy for the QBK) and the standard clinical prediction method, which are based on the basal SAPS score or logistic regression (71.32% and 71.55%, respectively). The QBK presented a sensitivity and specificity of 79.34% and 83.24%, which outperformed the other kernels analysed, logistic regression and the standard clinical prediction method based on the basal SAPS score. Conclusion: Several scoring systems for patients with sepsis have been introduced and developed over the last 30 years. They allow for the assessment of the severity of disease and provide an estimate of in-hospital mortality. Physiology-based scoring systems are applied to critically ill patients and have a number of advantages over diagnosis-based systems. Severity score systems are often used to stratify critically ill patients for possible inclusion in clinical trials. In this paper, we present an effective algorithm that combines both scoring methodologies for the assessment of death in patients with sepsis that can be used to improve the sensitivity and specificity of the currently available methods.

  • A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data

     Ortega Martorell, Sandra; Ruiz Ruiz, Hector Efrain; Vellido Alcacena, Alfredo; Olier, Ivan; Romero Merino, Enrique; Julia Sape, Margarida; Martin, Jose D.; Jarman, Ian H.; Arus, Carles; Lisboa, Paulo J G
    PLoS one
    Vol. 8, num. 12, p. e83773-1-e83773-14
    DOI: 10.1371/journal.pone.0083773
    Date of publication: 2013-12-23
    Journal article

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    Background: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. Methodology/Principal Findings: Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification...

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    Identifying useful human feedback from an on-line translation service  Open access

     Barron Cedeño, Luis Alberto; Màrquez Villodre, Lluís; Henriquez Quintana, Carlos Alberto; Formiga Fanals, Lluis; Romero Merino, Enrique; May, Jonathan
    International Joint Conference on Artificial Intelligence
    p. 1-7
    Presentation's date: 2013-08-07
    Presentation of work at congresses

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    Post-editing feedback provided by users of on-line translation services offers an excellent opportunity for automatic improvement of statistical machine translation (SMT) systems. However, feedback provided by casual users is very noisy, and must be automatically filtered in order to identify the poten- tially useful cases. We present a study on automatic feedback filtering in a real weblog collected from Reverso.net. We extend and re-annotate a train- ing corpus, define an extended set of simple fea- tures and approach the problem as a binary classi- fication task, experimenting with linear and kernel- based classifiers and feature selection. Results on the feedback filtering task show a significant im- provement over the majority class, but also a preci- sion ceiling around 70-80%. This reflects the inher- ent difficulty of the problem and indicates that shal- low features cannot fully capture the semantic na- ture of the problem. Despite the modest results on the filtering task, the classifiers are proven effective in an application-based evaluation. The incorpora- tion of a filtered set of feedback instances selected from a larger corpus significantly improves the per- formance of a phrase-based SMT system, accord- ing to a set of standard evaluation metrics

    Post-editing feedback provided by users of on-line translation services offers an excellent opportunity for automatic improvement of statistical machine translation (SMT) systems. However, feedback provided by casual users is very noisy, and must be automatically filtered in order to identify the potentially useful cases. We present a study on automatic feedback filtering in a real weblog collected from Reverso.net. We extend and re-annotate a training corpus, define an extended set of simple features and approach the problem as a binary classification task, experimenting with linear and kernelbased classifiers and feature selection. Results on the feedback filtering task show a significant improvement over the majority class, but also a precision ceiling around 70-80%. This reflects the inherent difficulty of the problemand indicates that shallow features cannot fully capture the semantic nature of the problem. Despite the modest results on the filtering task, the classifiers are proven effective in an application-based evaluation. The incorporation of a filtered set of feedback instances selected from a larger corpus significantly improves the performance of a phrase-based SMT system, according to a set of standard evaluation metrics.

  • On the Intelligent Management of Sepsis in the Intensive Care Unit  Open access

     Ribas Ripoll, Vicente Jorge
    Department of Computer Science, Universitat Politècnica de Catalunya
    Theses

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    . 41458682Vicente J. Ribas RipollOn the intelligent management of sepsis in the Intensive Care UnitLSI-SOCOAI120304La gestió d'una Unitat de Cures Intensives (UCI) hospitalària presenta uns requisits força específics incloent, entre altres, ladisminució de la taxa de mortalitat, la durada de l'ingrès, la rotació d'infermeres i la comunicació entre metges amb al finalitad dedonar una atenció de qualitat atenent als requisits tant dels malalts com dels familiars. També és força important controlar iminimitzar els error mèdics deguts a canvis sobtats i a la presa ràpida de deicisions assistencials. Al cap i a la fi, la bona gestióde la UCI hauria de resultar en una reducció de la mortalitat i durada d'estada.La presa de decisions en un entorn de crítics suposa un repte de presa de decisions en temps real d'acord a unes guiesclíniques molt restrictives i que, pel que fa a la recerca, poden resultar en problemes ètics força sensibles i complexos. Per tant,el personal sanitari que ha de prendre decisions sobre la gestió de malalts crítics no només requereix eines de suport a ladecisió que siguin fiables sinó que, a més a més, han de ser interpretables. Altrament qualsevol eina de decisió que no presentiaquests trets no és considerarà d'utilitat clínica.Aquesta tesi doctoral adreça aquests requisits mitjançant el desenvolupament d'eines de suport a la decisió per als intensivistes ies focalitza en un dels principals problemes als que s'han denfrontar: el maneig del malalt sèptic. La Sèpsia és una de lespprincipals causes de mortalitats a les UCIS no-coronàries i la seva taxa de mortalitat pot arribar fins a la meitat dels malalts ambxoc sèptic, la seva manifestació més severa. La Sèpsia és un síndrome transversal, que afecta a persones de totes les edats.Sorprenentment, la seva definició ha estat estandaritzada, fa només vint anys, com a la resposta inflamatòria sistèmica a unainfecció corfimada.La recerca presentada en aquest document fa referència a l'anàlisi de dades de la Sèpsia en general i, de forma més específica,al problema de la predicció de la supervivència de malalts afectats amb Sèpsia Greu. Les eines i mètodes que formen la clau debòveda d'aquest treball provenen de diversos camps com l'estadística multivariant i algebràica, geometria algebraica,aprenentatge automàtic i inteligència computacional.Més enllà de l'anàlisi per-se, aquesta tesi també presenta una contribució des de el punt de vista clínic atès que presentaevidència substancial en el debat sobre l'impacte de l'administració d'estatines previ a l'ingrès a la UCI en els malalts sèptics.També s'aclareix la forta dependència entre el xoc sèptic i el Síndrome de Disfunció Multiorgànica. Finalment, també es defineixun conjunt de descriptors latents de la Sèpsia com a factors de pronòstic per a la predicció de la mortalitat, que millora sobre elsmètodes actualment més utilitzats en la UCI.

    The management of the Intensive Care Unit (ICU) in a hospital has its own, very specific requirements that involve, amongst others, issues of risk-adjusted mortality and average length of stay; nurse turnover and communication with physicians; technical quality of care; the ability to meet patient's family needs; and avoid medical error due rapidly changing circumstances and work overload. In the end, good ICU management should lead to an improvement in patient outcomes. Decision making at the ICU environment is a real-time challenge that works according to very tight guidelines, which relate to often complex and sensitive research ethics issues. Clinicians in this context must act upon as much available information as possible, and could therefore, in general, benefit from at least partially automated computer-based decision support based on qualitative and quantitative information. Those taking executive decisions at ICUs will require methods that are not only reliable, but also, and this is a key issue, readily interpretable. Otherwise, any decision tool, regardless its sophistication and accuracy, risks being rendered useless. This thesis addresses this through the design and development of computer based decision making tools to assist clinicians at the ICU. It focuses on one of the main problems that they must face: the management of the Sepsis pathology. Sepsis is one of the main causes of death for non-coronary ICU patients. Its mortality rate can reach almost up to one out of two patients for septic shock, its most acute manifestation. It is a transversal condition affecting people of all ages. Surprisingly, its definition has only been standardized two decades ago as a systemic inflammatory response syndrome with confirmed infection. The research reported in this document deals with the problem of Sepsis data analysis in general and, more specifically, with the problem of survival prediction for patients affected with Severe Sepsis. The tools at the core of the investigated data analysis procedures stem from the fields of multivariate and algebraic statistics, algebraic geometry, machine learning and computational intelligence. Beyond data analysis itself, the current thesis makes contributions from a clinical point of view, as it provides substantial evidence to the debate about the impact of the preadmission use of statin drugs in the ICU outcome. It also sheds light into the dependence between Septic Shock and Multi Organic Dysfunction Syndrome. Moreover, it defines a latent set of Sepsis descriptors to be used as prognostic factors for the prediction of mortality and achieves an improvement on predictive capability over indicators currently in use.

    La gestió d'una Unitat de Cures Intensives (UCI) hospitalària presenta uns requisits força específics incloent, entre altres, la disminució de la taxa de mortalitat, la durada de l'ingrès, la rotació d'infermeres i la comunicació entre metges amb al finalitad de donar una atenció de qualitat atenent als requisits tant dels malalts com dels familiars. També és força important controlar i minimitzar els error mèdics deguts a canvis sobtats i a la presa ràpida de deicisions assistencials. Al cap i a la fi, la bona gestió de la UCI hauria de resultar en una reducció de la mortalitat i durada d'estada. La presa de decisions en un entorn de crítics suposa un repte de presa de decisions en temps real d'acord a unes guies clíniques molt restrictives i que, pel que fa a la recerca, poden resultar en problemes ètics força sensibles i complexos. Per tant, el personal sanitari que ha de prendre decisions sobre la gestió de malalts crítics no només requereix eines de suport a la decisió que siguin fiables sinó que, a més a més, han de ser interpretables. Altrament qualsevol eina de decisió que no presenti aquests trets no és considerarà d'utilitat clínica. Aquesta tesi doctoral adreça aquests requisits mitjançant el desenvolupament d'eines de suport a la decisió per als intensivistes i es focalitza en un dels principals problemes als que s'han denfrontar: el maneig del malalt sèptic. La Sèpsia és una de les principals causes de mortalitats a les UCIS no-coronàries i la seva taxa de mortalitat pot arribar fins a la meitat dels malalts amb xoc sèptic, la seva manifestació més severa. La Sèpsia és un síndrome transversal, que afecta a persones de totes les edats. Sorprenentment, la seva definició ha estat estandaritzada, fa només vint anys, com a la resposta inflamatòria sistèmica a una infecció corfimada. La recerca presentada en aquest document fa referència a l'anàlisi de dades de la Sèpsia en general i, de forma més específica, al problema de la predicció de la supervivència de malalts afectats amb Sèpsia Greu. Les eines i mètodes que formen la clau de bòveda d'aquest treball provenen de diversos camps com l'estadística multivariant i algebràica, geometria algebraica, aprenentatge automàtic i inteligència computacional. Més enllà de l'anàlisi per-se, aquesta tesi també presenta una contribució des de el punt de vista clínic atès que presenta evidència substancial en el debat sobre l'impacte de l'administració d'estatines previ a l'ingrès a la UCI en els malalts sèptics. També s'aclareix la forta dependència entre el xoc sèptic i el Síndrome de Disfunció Multiorgànica. Finalment, també es defineix un conjunt de descriptors latents de la Sèpsia com a factors de pronòstic per a la predicció de la mortalitat, que millora sobre els mètodes actualment més utilitzats en la UCI.

  • Analisi dell¿esposizione a rischi in una professione altamente variabile: il lavoro di imbianchino

     Álvarez Casado, Enrique; Romero Merino, Enrique
    Seminario Internazionale Prevenzione del Rischio da Sovraccarico Biomeccanico in Agricoltura e Edilizia
    Presentation's date: 2013-06-13
    Presentation of work at congresses

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    Identifying useful human correction feedback from an on-line machine translation service  Open access

     Barron Cedeño, Luis Alberto; Màrquez Villodre, Lluís; Henriquez, Carlos A; Formiga Fanals, Lluis; Romero Merino, Enrique; May, Jonathan
    International Joint Conference on Artificial Intelligence
    p. 2057-2063
    Presentation's date: 2013-08
    Presentation of work at congresses

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    Post-editing feedback provided by users of on-line translation services offers an excellent opportunity for automatic improvement of statistical machine translation (SMT) systems. However, feedback provided by casual users is very noisy, and must be automatically filtered in order to identify the potentially useful cases. We present a study on automatic feedback filtering in a real weblog collected from Reverso.net. We extend and re-annotate a training corpus, define an extended set of simple features and approach the problem as a binary classification task, experimenting with linear and kernelbased classifiers and feature selection. Results on the feedback filtering task show a significant improvement over the majority class, but also a precision ceiling around 70-80%. This reflects the inherent difficulty of the problem and indicates that shallow features cannot fully capture the semantic nature of the problem. Despite the modest results on the filtering task, the classifiers are proven effective in an application-based evaluation. The incorporation of a filtered set of feedback instances selected from a larger corpus significantly improves the performance of a phrase-based SMT system, according to a set of standard evaluation metrics.

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    A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients  Open access

     Ribas Ripoll, Vicent; Romero Merino, Enrique; Ruiz Rodriguez, Juan Carlos; Vellido Alcacena, Alfredo
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
    p. 379-384
    Presentation's date: 2013-04-24
    Presentation of work at congresses

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    In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. The QBK is used here for data transformation prior to classification in a medical problem concerning the prediction of mortality in patients suffering severe sepsis. This is a common clinical syndrome, often treated at the Intensive Care Unit (ICU) in a time-critical context. Mortality prediction results with Support Vector Machines using QBK compare favorably with those obtained using alternative kernels and standard clinical procedures.

  • Robust discrimination of glioblastomas from metastatic brain tumors on the basis of single-voxel 1H MRS

     Vellido Alcacena, Alfredo; Romero Merino, Enrique; Julià Sapé, Margarida; Majós, C.; Moreno Torres, À.; Pujol, Jesus; Arús, Carles
    NMR in biomedicine
    Vol. 25, num. 6, p. 819-828
    DOI: 10.1002/nbm.1797
    Date of publication: 2012-06
    Journal article

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  • Cohort-based kernel visualisation with scatter matrices

     Romero Merino, Enrique; Mu, Tingting; Lisboa, Paulo J.G.
    Pattern recognition
    Vol. 45, num. 4, p. 1436-1454
    DOI: 10.1016/j.patcog.2011.09.025
    Date of publication: 2012-04
    Journal article

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  • Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks

     Romero Merino, Enrique; Alquezar Mancho, Renato
    Neural networks
    Vol. 25, num. 1, p. 122-129
    DOI: 10.1016/j.neunet.2011.08.005
    Date of publication: 2012-01
    Journal article

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    Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one by one (or group by group) and update the output weights incrementally to minimize the sum-of-squares error in the training set. Other very similar methods that also construct SLFNs sequentially had been reported earlier with the main difference that their hidden-layer weights are a subset of the data instead of being random. These approaches are referred to as support vector sequential feed-forward neural networks (SV-SFNNs), and they are a particular case of the sequential approximation with optimal coefficients and interacting frequencies (SAOCIF) method. In this paper, it is firstly shown that EM-ELMs can also be cast as a particular case of SAOCIF. In particular, EM-ELMs can easily be extended to test some number of random candidates at each step and select the best of them, as SAOCIF does. Moreover, it is demonstrated that the cost of the computation of the optimal outputlayer weights in the originally proposed EM-ELMs can be improved if it is replaced by the one included in SAOCIF. Secondly, we present the results of an experimental study on 10 benchmark classification and 10 benchmark regression data sets, comparing EM-ELMs and SV-SFNNs, that was carried out under the same conditions for the two models. Although both models have the same (efficient) computational cost, a statistically significant improvement in generalization performance of SV-SFNNs vs. EM-ELMs was found in 12 out of the 20 benchmark problems.

  • SIGNAL PROCESSING TECHNIQUES FOR BRAIN TUMOUR DIAGNOSIS FROM MAGNETIC RESONANCE SPECTROSCOPY DATA

     Arizmendi Pereira, Carlos Julio
    Department of Computer Science, Universitat Politècnica de Catalunya
    Theses

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  • Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks

     Arizmendi Pereira, Carlos Julio; Vellido Alcacena, Alfredo; Romero Merino, Enrique
    Expert systems with applications
    Vol. 39, num. 5, p. 5223-5232
    DOI: 10.1016/j.eswa.2011.11.017
    Date of publication: 2012-04
    Journal article

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  • Data and knowledge visualization with virtual reality spaces, neural networks and rough sets: appplication to cancer and geophysical prospecting data

     Valdes Ramos, Julio Jose; Romero Merino, Enrique; Barton, Alan J.
    Expert systems with applications
    Vol. 39, num. 18, p. 13193-13201
    DOI: 10.1016/j.eswa.2012.05.082
    Date of publication: 2012-12-15
    Journal article

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  • Preprocessing MRS information for classification of human brain tumours

     Arizmendi Pereira, Carlos Julio; Vellido Alcacena, Alfredo; Romero Merino, Enrique
    Date of publication: 2012-06
    Book chapter

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  • Towards interpretable classifiers with blind signal separation

     Ruiz, Hector; Ortega Martorell, Sandra; Jarman, Ian H.; Vellido Alcacena, Alfredo; Martin Guerrero, Jose D.; Romero Merino, Enrique; Lisboa, Paulo J.G.
    International Conference on Neural Networks
    p. 1-7
    DOI: 10.1109/IJCNN.2012.6252783
    Presentation's date: 2012
    Presentation of work at congresses

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  • Binary classification of brain tumours using a discrete wavelet transform and energy criteria

     Arizmendi Pereira, Carlos Julio; Vellido Alcacena, Alfredo; Romero Merino, Enrique
    Latin American Symposium on Circuits and Systems
    p. 1-4
    DOI: 10.1109/LASCAS.2011.5750304
    Presentation's date: 2011-02
    Presentation of work at congresses

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  • Using the Leader algorithm with Support Vector Machines for large data sets

     Romero Merino, Enrique
    International Conference on Artificial Neural Networks
    p. 225-232
    DOI: 10.1007/978-3-642-21735-7_28
    Presentation's date: 2011-06
    Presentation of work at congresses

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  • Brain tumour classification using Gaussian decomposition and neural networks

     Arizmendi Pereira, Carlos Julio; Sierra Bueno, Daniel Alfonso; Vellido Alcacena, Alfredo; Romero Merino, Enrique
    IEEE Engineering in Medicine and Biology Society
    p. 5645-5648
    DOI: 10.1109/IEMBS.2011.6091366
    Presentation's date: 2011-08-30
    Presentation of work at congresses

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  • Feature and model selection with discriminatory visualization for diagnostic classification of brain tumors

     González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio; Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles
    Neurocomputing
    Vol. 73, num. 4-6, p. 622-632
    DOI: 10.1016/j.neucom.2009.07.018
    Date of publication: 2010-10
    Journal article

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  • Cohort-based kernel visualisation with scatter matrices

     Romero Merino, Enrique; Fernandes, Ana Sofia; Mu, Tingting; Lisboa, Paulo J.G.
    International Conference on Neural Networks
    p. 1249-1256
    DOI: 10.1109/IJCNN.2010.5596679
    Presentation's date: 2010-07-21
    Presentation of work at congresses

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  • Diagnosis of brain tumours from magnetic resonance spectroscopy using wavelets and neural networks

     Arizmendi Pereira, Carlos Julio; Hernández-Tamames, Juan; Romero Merino, Enrique; Vellido Alcacena, Alfredo; Pozo, Francisco del
    IEEE Engineering in Medicine and Biology Society
    p. 6074-6077
    DOI: 10.1109/IEMBS.2010.5627627
    Presentation's date: 2010-09-02
    Presentation of work at congresses

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  • Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks

     Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Diaz, I; Salvador Vales, Benito; Giraldo Giraldo, Beatriz F.
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Vol. 1, p. 4343-4346
    Date of publication: 2009
    Journal article

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  • Feature Selection with Single-Layer Perceptrons for a multicentre 1H-MRS brain tumour database

     Romero Merino, Enrique; Vellido Alcacena, Alfredo; Sopena, Josep Maria
    Lecture notes in computer science
    Vol. 5517, p. 1013-1020
    DOI: 10.1007/978-3-642-02478-8_127
    Date of publication: 2009-06-12
    Journal article

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    A Feature Selection process with Single-Layer Perceptrons is shown to provide optimum discrimination of an international, multi-centre 1H-MRS database of brain tumors at reasonable computational cost. Results are both intuitively interpretable and very accurate. The method remains simple enough as to allow its easy integration in existing medical decision support systems.

  • Outlier exploration and diagnostic classification of a multi-centre 1H-MRS brain tumour database

     Vellido Alcacena, Alfredo; Romero Merino, Enrique; González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio; Julià Sapé, Margarida; Arús, Carles
    Neurocomputing
    Vol. 72, num. 13-15, p. 3085-3097
    DOI: doi:10.1016/j.neucom.2009.03.010
    Date of publication: 2009
    Journal article

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  • An experimental study on methods for the selection of basis functions in regression

     Barrio Moliner, Ignacio; Romero Merino, Enrique; Belanche Muñoz, Luis Antonio
    Neurocomputing
    Vol. 72, num. SI:13-15, p. 2952-2963
    Date of publication: 2009-08
    Journal article

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  • Exploratory characterization of a multi-centre 1H-MRS brain tumour database

     Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Romero Merino, Enrique; Arús, Carles
    Date of publication: 2009-01-31
    Book chapter

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    Machine learning is a powerful paradigm within which to analyze 1HMRS spectral data for the classification of tumour pathologies. An important characteristic of this task is the high dimensionality of the involved data sets. In this work we apply specific feature selection methods in order to reduce the complexity of the problem on two types of 1H-MRS spectral data: long-echo and short-echo time, which present considerable differences in the spectrum for the same cases. The experimental findings show that the feature selection methods enhance the classification performance of the models induced by several off-the-shelf classifiers and are able to offer very attractive solutions both in terms of prediction accuracy and number of involved spectral frequencies.

  • Frequency selection for the diagnostic characterization of human brain tumours

     Arizmendi Pereira, Carlos Julio; Vellido Alcacena, Alfredo; Romero Merino, Enrique
    International Conference of the Catalan Association for Artificial Intelligence
    p. 391-398
    Presentation's date: 2009-10-22
    Presentation of work at congresses

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    Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks  Open access

     Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Díaz, Ivan; Benito Vales, Salvador; Giraldo Giraldo, Beatriz F.
    IEEE Engineering in Medicine and Biology Society
    p. 4343-4346
    DOI: 10.1109/IEMBS.2009.5332742
    Presentation's date: 2009-09
    Presentation of work at congresses

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    The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients.

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    Discriminating glioblastomas from metastases in a SV1H-MRS brain tumour database  Open access

     Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles
    Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology
    p. 18-19
    Presentation's date: 2009-10-02
    Presentation of work at congresses

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    A Feature Selection (FS) process with a simple Machine Learning method, namely the Single-Layer Perceptron (SLP), is shown to discriminate metastases from glioblastomas with high accuracy using single voxel H-MRS from an international, multi-centre database of brain tumors. The method has low computational cost and its results are intuitively interpretable.

    Postprint (author’s final draft)

  • Patients on weaning trials classified with neural networks and feature selection

     Giraldo Giraldo, Beatriz F.; Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Salvador Vales, Benito
    Date of publication: 2008-09
    Book chapter

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  • Performing Feature Selection with Multi-Layer Perceptrons

     Romero Merino, Enrique
    IEEE transactions on neural networks
    Vol. 19, num. 3, p. 431-441
    DOI: 10.1109/TNN.2007.909535
    Date of publication: 2008-03
    Journal article

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  • Exploratory characterization of outliers in a multi-centre 1H-MRS brain tumour dataset

     Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Romero Merino, Enrique; Arús, Carles
    Lecture notes in computer science
    Vol. 5178, p. 189-196
    DOI: 10.1007/978-3-540-85565-1_24
    Date of publication: 2008-09
    Journal article

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    As part of the AIDTumour research project, the analysis of MRS data corresponding to various tumour pathologies is used to assist expert diagnosis. The high dimensionality of the MR spectra might obscure atypical aspects of the data that would jeopardize their automated classification and, as a result, the process of computer-based diagnostic assistance. In this paper, we put forward a method to overcome this potential problem that combines automatic outlier detection, visualization through dimensionality reduction, and expert opinion.

  • Nonlinear dimensionality reduction for the exploration of outliers in a multicentre 1H-MRS database of brain tumours

     Vellido Alcacena, Alfredo; Romero Merino, Enrique
    ESMRMB 2008 Congress
    p. 16-17
    Presentation of work at congresses

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  • Exploring outlierness and its causes in a 1H-MRS brain tumour database

     Vellido Alcacena, Alfredo; Romero Merino, Enrique
    e-TUMOUR Workshop `Towards Brain Tumour Classification by Molecular Profiling?
    p. 40
    Presentation of work at congresses

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    Classification, dimensionality reduction, and maximally discriminatory visualization of a multicentre 1H-MRS database of brain tumors  Open access

     Lisboa, Paulo J.G.; Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles
    IEEE International Conference on Machine Learning and Applications
    p. 613-618
    DOI: 10.1109/ICMLA.2008.20
    Presentation's date: 2008
    Presentation of work at congresses

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    The combination of an Artificial Neural Network classifier, a feature selection process, and a novel linear dimensionality reduction technique that provides a data projection for visualization and which preserves completely the class discrimination achieved by the classifier, is applied in this study to the analysis of an international, multi-centre 1H-MRS database of brain tumors. This combination yields results that are both intuitively interpretable and very accurate. The method as a whole remains simple enough as to allow its easy integration in existing medical decision support systems.

  • DSS-oriented Exploration of a Multi-centre Magnetic Resonance Spectroscopy Brain Tumour Dataset Through Visualization

     Romero Merino, Enrique; Julià-Sapé, M; Vellido Alcacena, Alfredo
    European Symposium on Artificial Neural Networks
    p. 95-100
    Presentation of work at congresses

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  • Neural Network Based Visual Data Mining for Cancer Data

     Romero Merino, Enrique
    DOI: DOI: 10.4018/978-1-59904-849-9
    Date of publication: 2008-08-31
    Book chapter

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  • Neural Network Based Virtual Reality Spaces for Visual Data Mining of Cancer Data: An Unsupervised Perspective

     Romero Merino, Enrique
    Lecture notes in computer science
    Vol. 4507, p. 1020-1027
    Date of publication: 2007-06
    Journal article

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  • Extended linear models with gaussian prior on the parameters and adaptive expansion vectors

     Barrio Moliner, Ignacio; Romero Merino, Enrique; Belanche Muñoz, Luis Antonio
    Lecture notes in computer science
    Vol. 4668, p. 431-440
    Date of publication: 2007-09
    Journal article

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  • Selection of basis functions guided by the L2 soft margin

     Barrio Moliner, Ignacio; Romero Merino, Enrique; Belanche Muñoz, Luis Antonio
    Lecture notes in computer science
    Vol. 4668, p. 421-430
    Date of publication: 2007-09
    Journal article

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  • Heuristics for the selection of weights in sequential feed-forward neural networks: An experimental study

     Romero Merino, Enrique; Alquezar Mancho, Renato
    Neurocomputing
    Vol. 70, num. 16-18, p. 2735-2743
    DOI: 10.1016/j.neucom.2006.05.022
    Date of publication: 2007-10
    Journal article

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  • Comparing support vector machines and feedforward neural networks with similar hidden-layer weights

     Romero Merino, Enrique; Toppo, D
    IEEE transactions on neural networks
    Vol. 18, num. 3, p. 959-963
    Date of publication: 2007-05
    Journal article

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  • Incremental and decremental learning for linear support vector machines

     Romero Merino, Enrique; Barrio Moliner, Ignacio; Belanche Muñoz, Luis Antonio
    Lecture notes in computer science
    Vol. 4668, p. 209-218
    Date of publication: 2007-09
    Journal article

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  • Data and Knowledge Visualization with Virtual Reality Spaces, Neural Networks and Rough Sets: Application to Geophysical Prospecting

     Valdés, J J; Romero Merino, Enrique; González, R
    International Joint Conference on Neural Networks
    p. 1060-1065
    Presentation of work at congresses

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  • Eating Habits in a Spanish Adolescent Sample Analysed by Neural Networks

     Muro, P; Amador, J A; Romero Merino, Enrique; Sopena, J M
    General Meeting of the European Council on Eating Disorders
    p. 28
    Presentation of work at congresses

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  • Comparing Support Vector Machines and Feed-forward Neural Networks with Similar Parameters

     Romero Merino, Enrique
    Lecture notes in computer science
    Vol. 4224, p. 90-98
    Date of publication: 2006-09
    Journal article

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  • A sequential algorithm for feed-forward neural networks with optimal coefficients and interacting frequencies

     Romero Merino, Enrique; Alquezar Mancho, Renato
    Neurocomputing
    Vol. 69, num. 13-15, p. 1540-1552
    DOI: 10.1016/j.neucom.2005.07.006
    Date of publication: 2006-08
    Journal article

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  • Patients on weaning trials from mechanical ventilation classified with neural networks and feature selection

     Giraldo Giraldo, Beatriz F.; Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Benito Vales, Salvador; Ballesteros Carrillo, David
    IEEE Engineering in Medicine and Biology Society
    p. 2195-2198
    Presentation's date: 2006-08-30
    Presentation of work at congresses

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  • Clasificación de pacientes en proceso de extubacion mediante redes neuronales y selección características

     Giraldo Giraldo, Beatriz F.; Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquezar Mancho, Renato; Caminal Magrans, Pedro; Salvador Vales, Benito; Ballesteros Carrillo, David
    Congreso Anual de la Sociedad Española de Ingeniería Biomédica
    p. 281-284
    Presentation's date: 2006-11-06
    Presentation of work at congresses

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