The Experts below are selected from a list of 10797 Experts worldwide ranked by ideXlab platform
Chengxiang Zhai - One of the best experts on this subject based on the ideXlab platform.
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reliability prediction of webpages in the Medical Domain
European Conference on Information Retrieval, 2012Co-Authors: Parikshit Sondhi, V Vinod G Vydiswaran, Chengxiang ZhaiAbstract:In this paper, we study how to automatically predict reliability of web pages in the Medical Domain. Assessing reliability of online Medical information is especially critical as it may potentially influence vulnerable patients seeking help online. Unfortunately, there are no automated systems currently available that can classify a Medical webpage as being reliable, while manual assessment cannot scale up to process the large number of Medical pages on the Web. We propose a supervised learning approach to automatically predict reliability of Medical webpages. We developed a gold standard dataset using the standard reliability criteria defined by the Health on Net Foundation and systematically experimented with different link and content based feature sets. Our experiments show promising results with prediction accuracies of over 80%. We also show that our proposed prediction method is useful in applications such as reliability-based re-ranking and automatic website accreditation.
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ECIR - Reliability prediction of webpages in the Medical Domain
Lecture Notes in Computer Science, 2012Co-Authors: Parikshit Sondhi, V Vinod G Vydiswaran, Chengxiang ZhaiAbstract:In this paper, we study how to automatically predict reliability of web pages in the Medical Domain. Assessing reliability of online Medical information is especially critical as it may potentially influence vulnerable patients seeking help online. Unfortunately, there are no automated systems currently available that can classify a Medical webpage as being reliable, while manual assessment cannot scale up to process the large number of Medical pages on the Web. We propose a supervised learning approach to automatically predict reliability of Medical webpages. We developed a gold standard dataset using the standard reliability criteria defined by the Health on Net Foundation and systematically experimented with different link and content based feature sets. Our experiments show promising results with prediction accuracies of over 80%. We also show that our proposed prediction method is useful in applications such as reliability-based re-ranking and automatic website accreditation.
César Montes - One of the best experts on this subject based on the ideXlab platform.
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Discovering Similar Patterns for Characterizing Time Series in a Medical Domain
Knowledge and Information Systems, 2003Co-Authors: Fernando Alonso, P. Caraça-valente, Loïc Martínez, César MontesAbstract:In this article, we describe the process of discovering similar patterns in time series and creating reference models for population groups in a Medical Domain, and particularly in the field of physiotherapy, using data mining techniques on a set of isokinetic data. The discovered knowledge was evaluated against the expertise of a physician specialized in isokinetic techniques, and applied in the I4 (Intelligent Interpretation of Isokinetic Information) project developed in conjunction with the Spanish National Center for Sports Research and Sciences for muscular diagnosis and rehabilitation, injury prevention, training evaluation and planning, etc., of elite athletes and ordinary people.
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ICDM - Discovering similar patterns for characterising time series in a Medical Domain
Proceedings 2001 IEEE International Conference on Data Mining, 2001Co-Authors: Fernando Alonso, Loïc Martínez, J.p. Caraca-valente, César MontesAbstract:In this article, we describe the process of discovering similar patterns in time series and creating reference models for population groups in a Medical Domain, and particularly in the field of physiotherapy, using data mining techniques on a set of isokinetic data. The discovered knowledge was evaluated against the expertise of a physician specialising in isokinetic techniques, and applied in the I4 (Intelligent Interpretation of Isokinetic Information) project developed in conjunction with the Spanish National Centre for Sports Research and Sciences and the School of Physiotherapy of the Spanish National Organisation for the Blind for muscular diagnosis and rehabilitation, injury prevention, training evaluation and planning, etc., of elite and blind athletes.
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Discovering similar patterns for characterising time series in a Medical Domain
Proceedings 2001 IEEE International Conference on Data Mining, 2001Co-Authors: Fernando Alonso, Loïc Martínez, J.p. Caraca-valente, César MontesAbstract:In this article, we describe the process of discovering similar patterns in time series and creating reference models for population groups in a Medical Domain, and particularly in the field of physiotherapy, using data mining techniques on a set of isokinetic data. The discovered knowledge was evaluated against the expertise of a physician specialising in isokinetic techniques, and applied in the I4 (Intelligent Interpretation of Isokinetic Information) project developed in conjunction with the Spanish National Centre for Sports Research and Sciences and the School of Physiotherapy of the Spanish National Organisation for the Blind for muscular diagnosis and rehabilitation, injury prevention, training evaluation and planning, etc., of elite and blind athletes.
Olivier Ferret - One of the best experts on this subject based on the ideXlab platform.
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LREC - Learning Patterns for Building Resources about Semantic Relations in the Medical Domain.
2020Co-Authors: Mehdi Embarek, Olivier FerretAbstract:In this article, we present a method for extracting automatically from texts semantic relations in the Medical Domain using linguistic patterns. These patterns refer to three levels of information about words: inflected form, lemma and part-of-speech. The method we present consists first in identifying the entities that are part of the relations to extract, that is to say diseases, exams, treatments, drugs or symptoms. Thereafter, sentences that contain couples of entities are extracted and the presence of a semantic relation is validated by applying linguistic patterns. These patterns were previously learnt automatically from a manually annotated corpus by relying on an algorithm based on the edit distance. We first report the results of an evaluation of our Medical entity tagger for the five types of entities we have mentioned above and then, more globally, the results of an evaluation of our extraction method for four relations between these entities. Both evaluations were done for French.
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RIAO - Can Esculape cure the complex of œdipe in the Medical Domain
2010Co-Authors: Mehdi Embarek, Olivier FerretAbstract:In this article, we present Esculape, a question-answering system for French dedicated to family doctors and built from œdipe, an open-Domain system. Esculape adds to œdipe the capability to exploit the concepts and relations of a Domain model, the Medical Domain in the present case. Although a large number of resources exist in this Domain (UMLS, MeSH ...), it is not possible to rely only on them, and more specifically on the relations they contain, to answer questions. We show how this difficulty can be overcome by learning linguistic patterns for identifying relations and applying them to extract answers.
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Learning patterns for building resources about semantic relations in the Medical Domain
Sixth International Conference on Language Resources and Evaluation (LREC 2008), 2008Co-Authors: Mehdi Embarek, Olivier FerretAbstract:In this article, we present a method for extracting automatically from texts semantic relations in the Medical Domain using linguistic patterns. These patterns refer to three levels of information about words: inflected form, lemma and part-of-speech. The method we present consists first in identifying the entities that are part of the relations to extract, that is to say diseases, exams, treatments, drugs or symptoms. Thereafter, sentences that contain couples of entities are extracted and the presence of a semantic relation is validated by applying linguistic patterns. These patterns were previously learnt automatically from a manually annotated corpus by relying on an algorithm based on the edit distance. We first report the results of an evaluation of our Medical entity tagger for the five types of entities we have mentioned above and then, more globally, the results of an evaluation of our extraction method for four relations between these entities. Both evaluations were done for French.
Parikshit Sondhi - One of the best experts on this subject based on the ideXlab platform.
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reliability prediction of webpages in the Medical Domain
European Conference on Information Retrieval, 2012Co-Authors: Parikshit Sondhi, V Vinod G Vydiswaran, Chengxiang ZhaiAbstract:In this paper, we study how to automatically predict reliability of web pages in the Medical Domain. Assessing reliability of online Medical information is especially critical as it may potentially influence vulnerable patients seeking help online. Unfortunately, there are no automated systems currently available that can classify a Medical webpage as being reliable, while manual assessment cannot scale up to process the large number of Medical pages on the Web. We propose a supervised learning approach to automatically predict reliability of Medical webpages. We developed a gold standard dataset using the standard reliability criteria defined by the Health on Net Foundation and systematically experimented with different link and content based feature sets. Our experiments show promising results with prediction accuracies of over 80%. We also show that our proposed prediction method is useful in applications such as reliability-based re-ranking and automatic website accreditation.
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ECIR - Reliability prediction of webpages in the Medical Domain
Lecture Notes in Computer Science, 2012Co-Authors: Parikshit Sondhi, V Vinod G Vydiswaran, Chengxiang ZhaiAbstract:In this paper, we study how to automatically predict reliability of web pages in the Medical Domain. Assessing reliability of online Medical information is especially critical as it may potentially influence vulnerable patients seeking help online. Unfortunately, there are no automated systems currently available that can classify a Medical webpage as being reliable, while manual assessment cannot scale up to process the large number of Medical pages on the Web. We propose a supervised learning approach to automatically predict reliability of Medical webpages. We developed a gold standard dataset using the standard reliability criteria defined by the Health on Net Foundation and systematically experimented with different link and content based feature sets. Our experiments show promising results with prediction accuracies of over 80%. We also show that our proposed prediction method is useful in applications such as reliability-based re-ranking and automatic website accreditation.
K Thanushkodi - One of the best experts on this subject based on the ideXlab platform.
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A weighted bee colony optimisation hybrid with rough set reduct algorithm for feature selection in the Medical Domain
International Journal of Granular Computing Rough Sets and Intelligent Systems, 2011Co-Authors: N Suguna, K ThanushkodiAbstract:Feature selection refers to the problem of selecting the set of most relevant features which produces the most predictive outcome. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find the optimal subsets. This paper proposes a new feature selection method based on rough set theory hybrid with a weighted bee colony optimisation (WBCO) in an attempt to combat this. This proposed work is applied in the Medical Domain to find the minimal reducts and experimentally compared with the existing rough set methods, rough set methods with computational intelligence and non-rough set methods. The performance is analysed with a novel genetic algorithm-based k-nearest neighbour (GkNN) classifier. The experiments and results show that our proposed method could find optimum reducts than the other algorithms.
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a novel rough set reduct algorithm for Medical Domain based on bee colony optimization
arXiv: Learning, 2010Co-Authors: N Suguna, K ThanushkodiAbstract:Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt to combat this. This proposed work is applied in the Medical Domain to find the minimal reducts and experimentally compared with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).
