Knowledge Management for Health Care Procedures: From by David Riano PDF

By David Riano

This publication constitutes the completely refereed post-workshop court cases of the 1st AIME 2007 workshop From wisdom to international Care, K4CARE 2007, held in Amsterdam, The Netherlands, in July 2007, at the side of the eleventh overseas convention on man made Intelligence in medication, AIME 2007.

The 10 revised complete papers provided including 2 invited papers have been rigorously chosen in the course of a moment around of reviewing and development from 14 lectures given on the workshop and are awarded in prolonged model within the ebook. The papers are geared up in topical sections on healthiness care wisdom administration, overall healthiness care wisdom elicitation, well-being care wisdom transformation, and wellbeing and fitness care clever systems.

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Extra resources for Knowledge Management for Health Care Procedures: From Knowledge to Global Care, AIME 2007 Workshop K4CARE 2007, Amsterdam, The Netherlands, July 7, 2007, ...

Example text

Different methodologies have been designed according to the knowledge source [18]: texts, dictionaries, knowledge bases, semi-structured data, relation schemas, etc. Considering the nature of those classical repositories, the common characteristics of classical knowledge acquisition methods are: - - Many of them [6, 16] use as learning corpus a reduced and pre-selected set of relevant documents for the covered domain. This approach solves some problems about untrustiness, lack of structure, noise and size that arise when developing an unsupervised, domain-independent Web-based approach.

The rest of the paper is organized as follows. Section 2 presents an overview of the main steps involved in the ontology construction process, introducing the learning techniques employed for knowledge acquisition. Section 3 gives a general vision of our approach for learning domain ontologies from the Web, including the incremental acquisition of taxonomic and non-taxonomic relationships and named entities. Section 4 presents and evaluates an example of the obtained results for a medical domain framed in the context of the K4Care European research project.

MeSH3 and EMTREE4 are only two examples of controlled vocabularies used to index repositories in the biomedical domain. Biomedical terminologies and ontologies are also welcome as external resources supporting text mining tasks, such as entity recognition or relation extraction [6]. Biomedical ontologies provide a principled definition of biological and their interrelationships. Most text mining applications using these external resources have been carried out in the domain of molecular biology, although they have been also used to extract medical information from finding reports [7] or biological information from documents [8].

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