Information Technology in Bio- and Medical Informatics: by Jose Antonio Miñarro-Giménez, Teddy Miranda-Mena, Rodrigo

By Jose Antonio Miñarro-Giménez, Teddy Miranda-Mena, Rodrigo Martínez-Béjar (auth.), Christian Böhm, Sami Khuri, Lenka Lhotská, Nadia Pisanti (eds.)

This ebook constitutes the thorougly refereed court cases of the second one overseas convention on info know-how in Bio- and scientific Informatics, ITBAM 2011, held in Toulouse, France, in August/September 2011, at the side of DEXA 2011.

The thirteen lengthy papers and five brief papers have been rigorously chosen and tackle the subsequent themes: selection aid and information administration in biomedicine; clinical information mining and knowledge retrieval; workflow administration and choice help in medication; and category in bioinformatics. The papers convey how vast the spectrum of themes in purposes of data expertise to biomedical engineering and clinical informatics is.

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Additional resources for Information Technology in Bio- and Medical Informatics: Second International Conference, ITBAM 2011, Toulouse, France, August 31 - September 1, 2011. Proceedings

Sample text

For clustering, the ACO DTree method [3,2] and ACO inspired clustering [8] variations have been successfully used. Novel Nature Inspired Techniques in Medical Information Retrieval     "     "#   
      
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The first is the introduction of Markov models, the second one is that it uses sequential pattern mining, which takes into account the order of diseases in a patient medical record. The approach has been inspired by Khalil et al. [13] and adapted for the medical context. However there is a main difference in the way association rules are applied. CORE + computes association rules for the cluster the patient belongs to, while Khalil et al. find association rules only for the Markov states for which a prediction has not a sufficiently high probability.

13] and adapted for the medical context. However there is a main difference in the way association rules are applied. CORE + computes association rules for the cluster the patient belongs to, while Khalil et al. find association rules only for the Markov states for which a prediction has not a sufficiently high probability. Experimental results show that the integration of Markov models increases the prediction accuracy of CORE + with respect to CORE, and it is a promising method to predict individual diseases by taking into account only the illnesses a patient had in the past.

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