Multiple hierarchical classification of free-text clinical guidelines

Artificial Intelligence in Medicine

Volumn 37, Issue 3, 2006, Pages 177-190

  Moskovitch, Robert ^a   Cohen Kashi, Shiva ^a   Dror, Uzi ^a   Levy, Iftah ^a   Maimon, Amit ^a   Shahar, Yuval ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Clinical guidelines; Concept based search; Hierarchical Text Categorization; Information retrieval

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DECISION THEORY; INFORMATION RETRIEVAL; LEARNING ALGORITHMS;

CLASSIFICATION INDICES; CONCEPT-BASED SEARCH; FREE-TEXT CLINICAL GUIDELINES; HIERARCHICAL TEXT CATEGORIZATION;

HIERARCHICAL SYSTEMS;

ARTICLE; AUTOMATION; DECISION MAKING; DECOMPOSITION; EVALUATION; LEARNING; MEDICAL PRACTICE; PRACTICE GUIDELINE; PRIORITY JOURNAL;

ALGORITHMS; DATABASES, FACTUAL; HUMANS; INFORMATION STORAGE AND RETRIEVAL; NEURAL NETWORKS (COMPUTER); PRACTICE GUIDELINES;

EID: 33745132714     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2006.04.001     Document Type: Article

References (22)

1. Sebastiani F. Machine learning in automated text categorization. ACM Comput Surveys 34 1 (2002) 1-47
2. Grimshaw J.M., and Russel I.T. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet 342 (1993) 1317-1322
3. Peleg M., Tu S., Bury J., Ciccarese P., Fox J., Greenes R.A., et al. Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc 10 1 (2003) 52-68
4. Shahar Y., Young O., Shalom E., Galperin M., Mayaffit M., Moskovitch R., et al. A framework for a distributed, hybrid, multiple-ontology clinical-guideline library and automated guideline-support tools. J Biomed Inform 37 (2004) 25-344
5. Medical Subject Headings (MeSH), http://www.nlm.nih.gov/mesh/meshhome.html [accessed: 20 March 2006].
6. Humphreys B.L., and Lindberg D.A. The UMLS project: making the conceptual connection between users and the information they need. Bull Med Libr Assoc 81 2 (1993) 170-177
7. Moskovitch R., Hessing A., and Shahar Y. Vaidurya-a concept-based, context-sensitive search engine for clinical guidelines. Proceedings of the 2004 international medical informatics conference: MedInfo'04. San Francisco, CA, USA (2004)
8. (343/2005) R. Moskovitch, A concept-based, context-sensitive, multiple-ontology search engine for clinical guidelines. M.Sc. Thesis. Ben Gurion University, Israel; 2006.
9. Fuhr N., Hartmanna S., Lustig G., Schwantner M., and Tzeras K. Air/X-a rule-based multi-stage indexing system for large subject fields. Proceedings of the international conference of Recherche d'Information Assistée par Ordinateur (1991) 606-623
10. Joachims T. Text categorization with support vector machines: learning with many relevant features. Proceedings of the European conference on machine learning'98 (1998)
11. Yang Y. Expert network: effective and efficient learning from human decisions in text categorization and retrieval. Proceedings of the annual international conference of the ACM special interest group on information retrieval (1994) 13-22
12. Lewis D.D., and Ringuette M.A. A comparison of two learning algorithms for text categorization. Proceedings of the third annual symposium on document analysis and information retrieval (1994) 81-93
13. Weigend A.A., Wiener E.E., and Pedersen J.O. Exploiting hierarchy in text categorization. Inform Retrieval 1 3 (1999) 193-216
14. Dumais S.T., and Chen H. Hierarchical classification of web content. Proceedings of the annual international conference of the ACM special interest group on information retrieval (2000) 256-263
15. Sun A., and Lim E.-P. Hierarchical text classification and evaluation. Proceedings of the IEEE international conference on data mining (2001) 521-528
16. Hersh W.R., and Greenes R.A. SAPHIRE-an information retrieval system featuring concept matching, automatic indexing, probabilistic retrieval and hierarchical relationships. Comp Biomed Res 3 (1989) 410-425
17. Pratt W., and Fagan L. The usefulness of dynamically categorizing search results. J Am Med Inform Assoc 7 6 (2000) 605-617
18. Koller D., and Sahami M. Hierarchically classifying documents using very few words. Proceedings of the fourteenth international conference on machine learning (1997) 170-178
19. Ruiz M.E., and Srinivasan P. Hierarchical neural networks for text categorization. Proceedings of the annual international conference of the ACM special interest group on information retrieval (1999) 281-282
20. Salton G., Wong A., and Yang C.S. A vector space model for automatic indexing. Commun ACM 18 613 (1975) 620
21. Rocchio J.J. Relevance feedback in information retrieval. In: Salton G. (Ed). The SMART retrieval system: experiments in automatic document processing (1971), Prentice-Hall, Engelwood Clis, US 313-323
22. Porter M.F. An algorithm for suffix stripping. Program 14 3 (1980) 130-137