SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical neural networks for text categorization (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Exploiting Hierarchy in Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Journal of Biomedical Informatics
Methodological Review: Towards knowledge-based gene expression data mining
Journal of Biomedical Informatics
An Experiment in Automatic Classification of Pathological Reports
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Vaidurya: A multiple-ontology, concept-based, context-sensitive clinical-guideline search engine
Journal of Biomedical Informatics
Bayesian network models for hierarchical text classification from a thesaurus
International Journal of Approximate Reasoning
Guest editorial: Knowledge-based data analysis and interpretation
Artificial Intelligence in Medicine
Methodological Review: Computer-interpretable clinical guidelines: A methodological review
Journal of Biomedical Informatics
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Objective: Manual classification of free-text documents within a predefined hierarchy, commonly required in the medical domain, is highly time consuming task. We present an approach based on supervised learning to automate the classification of clinical guidelines into predefined hierarchical conceptual categories. Methods and material: Given a set of hierarchically categorized documents in the training stage the learning algorithm exploits the hierarchical structure of the concepts in order to overcome the low number of training examples. The classification task is thus decomposed into a continuous decision process, unlike searching within a decision tree, which follows the concept hierarchy and makes a single decision at each node on the path, multiple paths can be chosen. Classification is based on applying a similarity function at each concept. Several evaluation measures were used, based on the intended use of the hierarchy. In addition, conservative and aggressive stop-criterion strategies for stopping the search through the concept hierarchy were formulated. An evaluation of the approach, including several training methods and multiple evaluation measures, has been performed using a training set of 1136 guidelines from the National Guideline Clearing House set. Results: Based on a test collection consisting of 1038 clinical practice guidelines (CPGs) classified along two hierarchies, of roughly 5000 concepts, in which each CPG was classified by a mean of 10 concepts, a variable precision was observed from 44% to 60% depending on the settings of the training methods. Conclusion: These results demonstrate the feasibility of the approach, especially when considering the low ratio of guidelines to classification indices (concepts) in the evaluation data set used here.