Inferring appropriate eligibility criteria in clinical trial protocols without labeled data

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2012, Pages 21-28

  Restificar, Angelo ^a   Ananiadou, Sophia ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Clinical trials; Eligibility criteria; Unsupervised machine learning

Indexed keywords

CLINICAL TRIAL; ELIGIBILITY CRITERIA; EXPECTED VALUES; LABELED DATA; LATENT DIRICHLET ALLOCATIONS; LOGISTIC REGRESSION MODELS; RELEVANT DOCUMENTS; SCORING METHODS; UNSUPERVISED MACHINE LEARNING; USER QUERY; USER SIMULATION;

BIOINFORMATICS; DATA MINING; KNOWLEDGE MANAGEMENT; LOGISTICS; MEDICAL APPLICATIONS; PROBABILITY; REGRESSION ANALYSIS; STATISTICS;

EXPERIMENTS;

EID: 84870498957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2390068.2390074     Document Type: Conference Paper

References (24)

1. Berry de Bruijn, Simona Carini, Svetlana Kiritchenko, Joel Martin and Ida Sim. Automated Information Extraction of Key Trial Design Elements from Clinical Trial Publications. In AMIA 2008 Symposium Proceedings, pages 141-145, 2008.
2. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2007.
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research (JLMR), 3:993-1022, Jan 2003.
4. F. Boudin, J.-Y. Nie, and M. Dawes. Clinical information retrieval using document and pico structure. In Proceedings of the NAACL 2010, pages 822-830, Los Angeles, California, 2010. Association for Computational Linguistics.
5. Chintan O. Patel and James J. Cimino. Semantic Query Generation from Eligibility Criteria in Clinical Trials. In AMIA 2007 Symposium Proceedings, page 1070, 2007.
6. D. Demner-Fushman and J. Lin. Answering Clinical Questions with Knowledge-Based and Statistical Techniques. Computational Linguistics, 33:63-103, March 2007.
7. Dina Demner-Fushman and Jimmy Lin. Answer Extraction, Semantic Clustering, and Extractive Summarization for Clinical Question Answering. In ACL 2006 Proceedings, 2006.
8. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley, 2001.
9. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research (JLMR), 9:1871-1874, Aug 2008.
10. Hong Yu and Yong-gang Cao. Automatically Extracting Information Needs from Ad Hoc Clinical Questions. In AMIA 2008 Symposium Proceedings, pages 96-100, 2008.
11. Kenjiro Taura. GXP : An Interactive Shell for the Grid Environment. In International Workshop on Innovative Architecture for Future Generation High-Performance Processors and Systems, 2004.
12. S. N. Kim, D. Martinez, L. Cavedon, and L. Yencken. Automatic classification of sentences to support evidence based medicine. BMC Bioinformatics, 12(Suppl2), March 2011.
13. S. Kiritchenko, B. de Bruijn, S. Carini, J. Martin, and I. Sim. ExaCT: automatic extraction of clinical trial characteristics from journal publications. BMC Medical Informatics and Decision Making, 10:56, 2010.
14. I. Korkontzelos, T. Mu, and S. Ananiadou. ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials. BMC Medical Informatics and Decision Making, 12(Suppl 1):S3, 2012.
15. I. Korkontzelos, T. Mu, A. Restificar, and S. Ananiadou. Text mining for efficient search and assisted creation of clinical trials. In Proceedings of the ACM Fifth International Workshop on Data and Text Mining in Biomedical Informatics, pages 43-50, 2011.
16. C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method for large-scale logistic regression. Journal of Machine Learning Research (JLMR), 9:627-650, Apr 2008.
17. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11, 2009.
18. Mark Steyvers and Tom Griffiths. Probabilistic Topic Models. In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch, editors, Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum, 2006.
19. A. K. McCallum. MALLET: A Machine Learning for Language Toolkit. http://mallet.cs.umass.edu, 2002.
20. C. Schardt, M. B. Adams, T. Owens, S. Keitz, and P. Fontelo. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Medical Informatics and Decision Making, 7, 2007.
21. T. G. Dietterich. Machine Learning. In Nature Encyclopedia of Cognitive Science. Macmillan, 2003.
22. S. Tu, M. Peleg, S. Carini, M. Bobak, J. Ross, D. Rubin, and I. Sim. A Practical Method for Transforming Free-Text Eligibility Criteria into Computable Criteria. Journal of Biomedical Informatics, 44:239-250, 2011.
23. WS Richardson et al. The well-built clinical question: a key to evidence-based decisions. ACP Journal Club, 123, Nov-Dec 1995.
24. X. Huang, J. Lin and D. Demner-Fushman. PICO as a Knowledge Representation for Clinical Questions. In AMIA 2006 Symposium Proceedings, page 359?U363, 2006.