Developing topic-specific search filters for pubmed with click-through data

Methods of Information in Medicine

Volumn 52, Issue 5, 2013, Pages 395-402

  Li, J ^a,b   Lu, Z ^b  

^a INSTITUTE OF MEDICAL INFORMATION   (China)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Clinical topic; Information retrieval; Pubmed log analysis; Pubmed search filter

Indexed keywords

ACCESS TO INFORMATION; ARTICLE; CLINICAL TOPIC; COMPUTER INTERFACE; DATA MINING; INFORMATION RETRIEVAL; MEDICAL SUBJECT HEADINGS; MEDLINE; METHODOLOGY; PUBMED LOG ANALYSIS; PUBMED SEARCH FILTER; STANDARD; UNITED STATES; WEB BROWSER;

CLINICAL TOPIC; INFORMATION RETRIEVAL; PUBMED LOG ANALYSIS; PUBMED SEARCH FILTER;

ACCESS TO INFORMATION; DATA MINING; MEDICAL SUBJECT HEADINGS; PUBMED; UNITED STATES; USER-COMPUTER INTERFACE; WEB BROWSER;

EID: 84886460135     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.3414/ME12-01-0054     Document Type: Article

References (38)

1. Shariff SZ, Sontrop JM, Haynes RB, Iansavichus AV, McKibbon KA, Wilczynski NL et al. Impact of PubMed search filters on the retrieval of evidence by physicians. CMAJ 2012; 184 (3): E184 -E190.
2. Lee E, Dobbins M, Decorby K, McRae L, Tirilis D, Husson H. An optimal search filter for retrieving systematic reviews and meta-analyses. BMC Med Res Methodol 2012; 12: 51.
3. Golder S, Loke YK. The performance of adverse effects search filters in MEDLINE and EMBASE. Health Info Libr J 2012; 29 (2): 141-151.
4. Jenkins M. Evaluation of methodological search filters--a review. Health Info Libr J 2004; 21 (3): 148 -163.
5. PubMed's Clinical Queries [internet]. Bethesda (MD): National Library of Medicine (US) (cited 2012 Dec). Available from: http://www.ncbi.nlm.nih.gov/pubmed/clinical
6. Garg AX, Iansavichus AV, Wilczynski NL, Kastner M, Baier LA, Shariff SZ et al. Filtering Medline for a clinical discipline: diagnostic test assessment framework. BMJ 2009; 339: b3435.
7. Iansavichus AV, Haynes RB, Shariff SZ, Weir M, Wilczynski NL, McKibbon KA et al. Optimal search filters for renal information in EMBASE. Am J of Kidney Dis 2010; 56 (1): 14 -22.
8. van de Glind EM, van Munster BC, Spijker R, Scholten RJ, Hooft L. Search filters to identify geriatric medicine in Medline. J Am Med Inform Assoc 2012; 19 (3): 468 - 472.
9. Kastner M, Wilczynski NL, Walker-Dilks C, McKibbon KA, Haynes RB. Age-specific search strategies for Medline. J Med Internet Res 2006; 8 (4): e25.
10. Beahler CC, Sundheim JJ, Trapp NI. Information retrieval in systematic reviews: challenges in the public health arena. Am J Prev Med 2000; 18 (4 Suppl): 6-10.
11. Goss C, Lowenstein S, Roberts I, Diguiseppi C. Identifying controlled studies of alcohol-impaired driving prevention: designing an effective search strategy. J Inf Sci 2007; 33 (2): 151-162.
12. Mesgarpour B, Muller M, Herkner H. Search strategies to identify reports on "off-label" drug use in EMBASE. BMC Med Res Methodol 2012; 12: 190.
13. Lu Z, Xie N, Wilbur WJ. Identifying related journals through log analysis. Bioinformatics 2009; 25 (22): 3038 -3039.
14. Lu Z, Wilbur WJ, McEntyre JR, Iskhakov A, Szilagyi L. Finding query suggestions for PubMed. Proceedings of the American Medical Informatics Association 2009 Annual Symposium; 2009 Nov 14 -18; San Francisco, USA. AMIA; 2009.
15. Islamaj Dogan R, Lu Z. Click-words: learning to predict document keywords from a user perspective. Bioinformatics 2010; 26 (21): 2767-2775.
16. Neveol A, Islamaj Dogan R, Lu Z. Semi-automatic semantic annotation of PubMed queries: a study on quality, efficiency, satisfaction. J Biomed Inform 2011; 44(2): 310-318.
17. Wilczynski NL, Morgan D, Haynes RB. An overview of the design and methods for retrieving high-quality studies for clinical care. BMC Med Inform Decis Mak 2005; 5: 20.
18. Medical Subject Headings (MeSH®) [internet]. Bethesda (MD): National Library of Medicine (US) (cited 2012 Dec). Available from: http://www.nlm.nih.gov/mesh/
19. Corrao S, Colomba D, Arnone S, Argano C, Di Chiara T, Scaglione R et al. Improving efficacy of PubMed Clinical Queries for retrieving scientifically strong studies on treatment. J Am Med Inform Assoc 2006; 13(5): 485-487.
20. Islamaj Dogan R, Murray GC, Neveol A, Lu Z. Understanding PubMed user search behavior through log analysis. Database (Oxford) 2009; 2009: bap018.
21. Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proceedings of the American Medical Informatics Association 2001 Annual Symposium; 2001 Nov 3-7; Washington DC, USA. AMIA; 2001
22. MetaMap [internet]. Bethesda (MD): National Library of Medicine (US) (cited 2012 Dec). Available from: http://metamap.nlm.nih.gov/
23. Unified Medical Language System ® (UMLS®) [internet]. Bethesda (MD): National Library of Medicine (US) (cited 2012 Dec). Available from: http://www.nlm.nih.gov/research/umls/
24. Core clinical journals [internet]. Bethesda (MD): National Library of Medicine (US) (cited 2012 Dec). Available from: http://www.nlm.nih.gov/bsd/aim.html
25. Croft WB, Metzler D, Strohman T. Search engines: information retrieval in practice. 2nd ed. Pearson Education Inc; 2010.
26. Indexed Field in PubMed (internet). Bethesda (MD): National Library of Medicine (US) (cited 2012 Dec). Available from: http://www.ncbi.nlm.nih.gov/books/NBK3827/#pubmedhelp.Search_Field_Descrip
27. McCray AT, Gefeller O, Aronsky D, Leong TY, Sarkar IN, Bergemann D et al. The birth and evolution of a discipline devoted to information in biomedicine and health care. As reflected in its longest running journal. Methods Inf Med 2011; 50 (6): 491-507.
28. Kastrin A, Peterlin B, Hristovski D. Chi-squarebased scoring function for categorization of MEDLINE citations. Methods Inf Med 2010; 49 (4): 371-378.
29. Yen YT, Chen B, Chiu HW, Lee YC, Li YC, Hsu CY. Developing an NLP and IR-based algorithm for analyzing gene-disease relationships. Methods Inf Med 2006; 45 (3): 321-329.
30. Neveol A, Islamaj Dogan R, Lu Z. Author keywords in biomedical journal articles. Proceedings of the American Medical Informatics Association 2010 Annual Symposium; 2010 Nov 13-17; Washington DC, USA. AMIA; 2010.
31. Lu Z, Kim W, Wilbur WJ. Evaluation of query expansion using MeSH in PubMed. Inf Retr Boston 2009; 12 (1): 69 - 80.
32. Robinson KA, Dickersin K. Development of a highly sensitive search strategy for the retrieval of reports of controlled trials using PubMed. Int J Epidemiol 2002; 31 (1): 150 -153.
33. Huang M, Neveol A, Lu Z. Recommending MeSH terms for annotating biomedical articles. J Am Med Inform Assoc 2011; 18 (5): 660 - 667.
34. Macdonald C, Ounis I. Usefulness of quality clickthrough data for training. Proceedings of the Workshop on Web Search Click Data; 2009 Feb 9; Barcelona, Spain. ACM; 2009.
35. Joachims T. Optimizing search engines using clickthrough data. Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining; 2002 Jul 23 -26; Edmonton, Alberta, Canada. ACM SIGKDD; 2002.
36. Joachims T, Granka L, Pan B, Hembrooke H, Gay G. Accurately interpreting clickthrough data as implicit feedback. Proceedings of the 28th International Conference on Research and Development in Information Retrieval; 2005 Aug 15 -19; Salvador, Brazil. ACM SIGIR; 2005.
37. Carterette B, Jones R. Evaluating search engines by modeling the relationship between relevance and clicks. Proceedings of the 21st Annual Conference on Neural Information Processing Systems; 2007 Dec 3-6; Vancouver, B.C., Canada. NIPS; 2007.
38. Xu J, Chen C, Xu G, Li H, Abib ERT. Improving quality of training data for learning to rank using click-through data. Proceedings of the 3rd International Conference on Web Search and Data Mining; 2010 Feb 3 - 6; New York, USA. ACM WSDM; 2010.