Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Hierarchical Classification of Documents with Error Control
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Clustering documents in a web directory
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Bootstrapping for hierarchical document classification
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Hierarchical classification of HTML documents with WebClassII
ECIR'03 Proceedings of the 25th European conference on IR research
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Clinical Practice Guidelines (CPGs) are increasingly common in clinical medicine for prescribing a set of rules that a physician should follow. Recent interest is in accurate retrieval of CPGs at the point of care. Examples are the CPGs digital libraries National Guideline Clearinghouse (NGC) or Vaidurya, which are organized along predefined concept hierarchies. In this case, both browsing and concept-based search can be applied. However, mandatory step in enabling both ways to CPGs retrieval is manual classification of CPGs along the concepts hierarchy, which is extremely time consuming. Supervised learning approaches are usually not satisfying, since commonly too few or no CPGs are provided as training set for each class. In this paper we apply TaxSOM for multiple classification. TaxSOM is an unsupervised model that supports the physician in the classification of CPGs along the concepts hierarchy, even when no labeled examples are available. This model exploits lexical and topological information on the hierarchy to elaborate a classification hypothesis for any given CPG. We argue that such a kind of unsupervised classification can support a physician to classify CPGs by recommending the most probable classes. An experimental evaluation on various concept hierarchies with hundreds of CPGs and categories provides the empirical evidence of the proposed technique.