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Articles

A model based on multi-features to enhance healthcare and medical document retrieval

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Pages 100-115 | Published online: 08 Dec 2010

References

  • Betin Can A, Baykal N. MedicoPort: a medical search engine for all. Computer Methods and Programs in Biomedicine 2007;86:73–86.
  • Yildiz M, Pratt W. The effect of feature representation on MEDLINE Document Classification. In: Proceedings of the American Medical Informatics Association Fall Symposium, Washington, D.C.; 2005, pp. 849–853.
  • Newton I. Philosophiae Naturalis Principia Mathematica. 3rd ed, London, England: Henry Pemberton; 1726.
  • Hersh W, Buckley C, Leone T, Hickman D. OHSUMED: an interactive retrieval evaluation and new large text collection for research. In: Croft WB, van Rijsbergen CJ, editors.Proceedings of the 17th annual international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR ′94), Dublin, Ireland; 1994, pp. 192–201.
  • Robertson S, Hull DA. The TREC-9 filtering track final report. In: Voorhees EM, Harman DK, Editors.Proceedings of the Ninth Text REtrieval Conference (TREC-9), Gaithersburg, Maryland: Department of Commerce, National Institute of Standards and Technology; 2000, pp. 25–40.
  • Geng X, Liu T, Qin T, Li H. Feature selection for ranking. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Amsterdam, Netherlands; 2007, pp. 407–441.
  • Chagoyen M, Carmona-Saez P, Shatkay H, Caraz M, Pascual-Montano A. Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinformatics 2006;7:41. DOI:10.1186/1471-2105-7-41.
  • Dumais T, Platt J, Heckerman D, Sahami M. Inductive learning algorithms for text categorization. In: Proceedings of the 7th Conference on Information and Knowledge Management, Bethesda, Maryland; 1998, pp. 148–155.
  • Lewis D. An evaluation of phrasal and clustered representations on a text categorization task. In: Proceedings of the 15th ACM International Conference on Research and Development in Information Retrieval, Copenhagen, Denmark; 1992, pp. 37–50.
  • Charniak E. Statistical language learning. CambridgeMIT Press1993.
  • Jelinek F. Statistical methods for speech recognition. CambridgeMIT Press1997.
  • Mao W, Chu W. Free-text medical document retrieval via phrase-based vector space model. In: Proceedings of the Annual AMIA SymposiumSan Antonio, TX2002pp. 489–493.
  • Boyack K, Mane K, Börner K. Mapping medline papers, genes, and proteins related to melanoma research. IV2004 Conference. London, UK; 2004, pp. 965–971.
  • Lin J. PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval. BMC Bioinformatics 2008;9:270. DOI: 10.1186/1471-2105-9-270.
  • Lin J, DiCuccio M, Grigoryan V, Wilbur W. Exploring the effectiveness of related article search in PubMed. Technical Report CS-TR-4877/UMIACS-TR-2007-36/HCIL-2007-10College Park, MarylandUniversity of Maryland2007.
  • Robertson S, Walker S. Microsoft Cambridge at TREC-9: filtering track. In: Voorhees EM, Harman DK, editors.Proceedings of the Ninth Text REtrieval Conference (TREC-9), Gaithersburg, Maryland: Department of Commerce, National Institute of Standards and Technology; 2001, pp. 361–368.
  • Ault T, Yang Y. Information filtering in TREC-9 and TDT-3: a comparative analysis. Information Retrieval 2002;5:159–187. DOI 10.1023/A:101574591176.
  • Stricker M, Vichot F, Dreyfus G, Wolinski F. Training context-sensitive neural networks with few relevant examples for the trec-9 routing. In: The Ninth Text REtrieval Conference (TREC9), Gaithersburg, Maryland: National Institute of Standards and Technology, special publication; 2000, pp. 257–262.
  • OMNI Medical Search. Available from: www.omnimedicalsearch.com (accessed 23 April 2009).
  • Health On the Net foundation. Available from: http://www.hon.ch/MedHunt/ (accessed 23 April 2009).
  • Lin Y, Li W, Chen K, Liu Y. A document clustering and ranking system for exploring MEDLINE citations. Journal of the American Medical Informatics Association 2007;14:651–661. DOI 10.1197/jamia.M2215.
  • Lu Z, Kim W, Wilbu W. Evaluating relevance Ranking strategies for MEDLINE retrieval. Journal of the American Medical Informatics Association 2009;16:32–36. DOI 10.1197/jamia.M2935.
  • PubMed, a service of the US national library of medicine and the national institutes of health. Available from: http://www.ncbi.nlm.nih.gov/pubmed/ (accessed 23 April 2009).
  • WebMD. Available from: http://www.webmd.com (accessed 23 April 2009).
  • MedlinePlus, a service of the US national library of medicine and the national institutes of health. Available from: http://medlineplus.gov (accessed 12 June 2008).
  • Siadaty M, Shu J, Knaus W. Relemed: sentence-level search engine with relevance score for the MEDLINE database of biomedical articles. BMC Medical Informatics and Decision Making 2007;7:1. DOI:10.1186/1472-6947-7-1.
  • Ide N, Loane R, Demner-Fushman D. Essie: a concept-based search engine for structured biomedical text. Journal of the American Medical Informatics Association 2007;14:253–263. DOI 10.1197/jamia.M2233.
  • Ruch GP, Joubert M, Uziel P, Strauss A, Thonnet M, Baud R, Spahni S, Weber P, Bonal J. Health search engine with e-document analysis for reliable search results. International Journal of Medical Informatics 2006;75:73–85.
  • Dietze H, Schroeder M. GoWeb: a semantic search engine for the life science web. BMC Bioinformatics 2009;10 (Suppl 10):S7. doi: 10.1186/1471-2105-10-S10-S7.
  • National Library of Medicine. Unified Medical Language System Fact Sheet. Available at: http://www.nlm.nih.gov/pubs/factsheets/umls.html (accessed 12 June 2008).
  • Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 1998;30:107–117.
  • Craswell N, Hawking D, Robertson S, Effective site finding using link anchor information. Annual ACM Conference on Research and Development in Information Retrieval archive Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, New Orleans, Louisiana; 2001, pp. 250–257.
  • Baeza-Yates R. Berthier Ribeiro-Neto. Modern Information RetrievalNew YorkACM press1999.
  • Ahmad K, Gillam L, Tostevin L. Weirdness indexing for logical document extrapolation and retrieval (WILDER). In: Voorhees EM, Harman DK, editors.The 8th Text Retrieval Conference (TREC-8), Washington: National Institute of Standards and Technology; 2000, pp. 717–724.
  • English Stopword List. Available at: ftp://ftp.cs.cornell.edu/pub/smart/english.stop (accessed 20 June 20 2008).
  • Lau A, Coiera E. Can cognitive biases during consumer health information searches be reduced to improve decision making? Journal of the American Medical Informatics Association 2009;16:54–65. DOI 10.1197/jamia. M2557.

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