Multiple hierarchical classification of free-text clinical guidelines

References 

1. Sebastiani F. Machine learning in automated text categorization. ACM Comput Surveys. 2002;34(1):1–47
2. Grimshaw JM, Russel IT. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet. 1993;342:1317–1322
   • Abstract
   • CrossRef
3. Peleg M, Tu S, Bury J, Ciccarese P, Fox J, Greenes RA, et al. Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc. 2003;10(1):52–68
   • MEDLINE
   • CrossRef
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. 2004;37:25–344
5. Medical Subject Headings (MeSH), http://www.nlm.nih.gov/mesh/meshhome.html [accessed: 20 March 2006].
6. Humphreys BL, Lindberg DA. The UMLS project: making the conceptual connection between users and the information they need. Bull Med Libr Assoc. 1993;81(2):170–177
   • MEDLINE
7. Moskovitch R, Hessing A, Shahar Y. Vaidurya—a concept-based, context-sensitive search engine for clinical guidelines. In: 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, Tzeras K. Air/X—a rule-based multi-stage indexing system for large subject fields. In: Proceedings of the international conference of Recherche d’Information Assistée par Ordinateur. 1991;p. 606–623
10. Joachims T. Text categorization with support vector machines: learning with many relevant features. In: 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. In: Proceedings of the annual international conference of the ACM special interest group on information retrieval. 1994;p. 13–22
12. Lewis DD, Ringuette MA. A comparison of two learning algorithms for text categorization. In: Proceedings of the third annual symposium on document analysis and information retrieval. 1994;p. 81–93
13. Weigend AA, Wiener EE, Pedersen JO. Exploiting hierarchy in text categorization. Inform Retrieval. 1999;1(3):193–216
14. Dumais ST, Chen H. Hierarchical classification of web content. In: Proceedings of the annual international conference of the ACM special interest group on information retrieval. 2000;p. 256–263
15. Sun A, Lim E-P. Hierarchical text classification and evaluation. In: Proceedings of the IEEE international conference on data mining. 2001;p. 521–528
16. Hersh WR, Greenes RA. SAPHIRE—an information retrieval system featuring concept matching, automatic indexing, probabilistic retrieval and hierarchical relationships. Comp Biomed Res. 1989;3:410–425
17. Pratt W, Fagan L. The usefulness of dynamically categorizing search results. J Am Med Inform Assoc. 2000;7(6):605–617
    • MEDLINE
    • CrossRef
18. Koller D, Sahami M. Hierarchically classifying documents using very few words. In: Proceedings of the fourteenth international conference on machine learning. 1997;p. 170–178
19. Ruiz ME, Srinivasan P. Hierarchical neural networks for text categorization. In: Proceedings of the annual international conference of the ACM special interest group on information retrieval. 1999;p. 281–282
20. Salton G, Wong A, Yang CS. A vector space model for automatic indexing. Commun ACM. 1975;18(613):620
21. Rocchio JJ. Relevance feedback in information retrieval. In:  Salton G editors. The SMART retrieval system: experiments in automatic document processing. Engelwood Clis, US: Prentice-Hall; 1971;p. 313–323
22. Porter MF. An algorithm for suffix stripping. Program. 1980;14(3):130–137