Dynamic categorization of clinical research eligibility criteria by hierarchical clustering

Journal of Biomedical Informatics

Volumn 44, Issue 6, 2011, Pages 927-935

  Luo, Zhihui ^a   Yetisgen Yildiz, Meliha ^b   Weng, Chunhua ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Classification; Clinical research eligibility criteria; Feature representation; Hierarchical clustering; Knowledge representation; Machine learning; Unified Medical Language System (UMLS)

Indexed keywords

CLINICAL RESEARCH; FEATURE REPRESENTATION; HIER-ARCHICAL CLUSTERING; MACHINE-LEARNING; UNIFIED MEDICAL LANGUAGE SYSTEMS;

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); KNOWLEDGE REPRESENTATION; RESEARCH; SEMANTICS;

CLASSIFIERS;

ACCURACY; ARTICLE; BAYESIAN LEARNING; CLASSIFICATION; CLINICAL RESEARCH ELIGIBILITY CRITERIA; CLUSTER ANALYSIS; COMPUTER INTERFACE; CONCEPTUAL FRAMEWORK; HIERARCHICAL CLUSTERING; KNOWLEDGE; MEDICAL INFORMATION SYSTEM; PRIORITY JOURNAL; RELIABILITY; SEMANTICS; STANDARDIZATION; VALIDITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMEDICAL RESEARCH; CLUSTER ANALYSIS; SEMANTICS; UNIFIED MEDICAL LANGUAGE SYSTEM;

EID: 84855953110     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2011.06.001     Document Type: Article

References (36)

1. Weng C., Tu S.W., Sim I., Richesson R. Formal representation of eligibility criteria: a literature review. J Biomed Inform 2010, 43(3):451-467.
2. McCray A.T. Better access to information about clinical trials. Ann Intern Med 2000, 133(8):609-614.
3. Sim I., Olasov B., Carini S. An ontology of randomized controlled trials for evidence-based practice: content specification and evaluation using the competency decomposition method. J Biomed Inform 2004, 37(2):108-119.
4. Tu S.W., Peleg M., Carini S., Bobak M., Ross J., Rubin D., et al. A practical method for transforming free-text eligibility criteria into computable criteria. J Biomed Inform 2011, 239(2):239-250.
5. Niland J, Cohen E. ASPIRE: agreement on standardized protocol inclusion requirements for eligibility; 2007.
6. Tu S.W. A methodology for determining patients' eligibility for clinical trials. Methods Inf Med 1993, 32(4):317-325.
7. Metz J.M., Coyle C., Hudson C., Hampshire M. An internet-based cancer clinical trials matching resource. J Med Int Res 2005, 7(3):e24.
8. Rubin R.R. The diabetes prevention program: recruitment methods and results. Control Clin Trials 2002, 23(2):157-171.
9. Tassignon J-P, Sinackevich N. Speeding the critical path. Applied Clinical Trials; 2004.
10. Ross J, Tu S, Carini S, Sim I. Analysis of eligibility criteria complexity in clinical trials. San Francisco, California: AMIA Summit on Clinical Research Informatics; 2010. p. 46-50.
11. Luo Z, Johnson SB, Weng C. Semi-automatic induction of semantic classes from free-text clinical research eligibility criteria using UMLS. In: American Medical Informatics Association annual symposium. Washington, DC; 2010. p. 487-91.
12. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I. The WEKA data mining software: an update. SIGKDD Explor Newsl 2009, 11(1):10-18.
13. Quinlan R. C4.5: Programs for machine learning (Morgan Kaufmann Series in Machine Learning). Morgan Kaufmann; 1993.
14. Cooper G., Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 1992, 9(4):309-347.
15. John G, Langley P. Estimating continuous distributions in bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence; 1995. p. 338-45.
16. Martin B. Instance-based learning: nearest neighbor with generalization. Hamilton, New Zealand: Department of Computer Science, University of Waikato; 1995.
17. Aha D.W., Kibler D., Albert M.K. Instance-based learning algorithms. Mach Learn 1991, 06(1):37-66.
18. Luo Z, Duffy R, Johnson S, Weng C. Corpus-based approach to creating a semantic lexicon for clinical research eligibility criteria from UMLS. San Francisco, California: AMIA Summit on Clinical Research Informatics; 2010. p. 26-31.
19. Johnson S.B. A semantic lexicon for medical language processing. J Am Med Inform Assoc 1999, 6(3):205-218.
20. Johnson S.C. Hierarchical clustering schemes. Psychometrika 1967, 2:241-254.
21. Pearson K. Mathematical contributions to the theory of evolution. XI. On the influence of natural selection on the variability and correlation of organs. Philos Trans Roy Soc Lond Ser A: Math Phys Charact 1903;200:1-66 (ArticleType: research-article/Full publication date: 1903/Copyright © 1903 The Royal Society).
22. Carletta J. Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 1996, 22(2):249-254.
23. Zimmerman D.W. A note on interpretation of the paired-samples t test. J Educ Behav Stat 1997, 22(3):349-360.
24. Weng C, Luo Z. Dynamic categorization of clinical research eligibility criteria. In: Proceedings of AMIA fall symposium; 2010. p. 306.
25. Pratt W., Fagan L. The usefulness of dynamically categorizing search results. J Am Med Inform Assoc 2000, 7(6):605-617.
26. Broughton V. Faceted classification as a basis for knowledge organization in a digital environment; the bliss bibliographic classification as a model for vocabulary management and the creation of multidimensional knowledge structures. New Rev Hypermedia Multimedia 2001, 7(1):67-102.
27. Mitchell TM. Machine learning. McGraw-Hill Science/Engineering/Math; 1997. p. 432.
28. Nigam K., McCallum A., Thrun S., Mitchell T. Text classification from labeled and unlabeled documents using EM. Mach Learn 2000, 39(2):103-134.
29. Liu B, Li X, Lee WS, Yu PS. Text classification by labeling words. In: Proceedings of the 19th national conference on artificial intelligence. San Jose, California; 2004. p. 425-30.
30. Yetisgen-Yildiz M, Pratt W. The effect of feature representation on MEDLINE document classification. In: AMIA annual symposium proceedings; 2005. p. 849-53.
31. Agarwal S., Yu H. Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion. Bioinformatics 2009, 25(23):3174-3180.
32. McKnight L, Srinivasan P. Categorization of sentence types in medical abstracts. In: AMIA annual symposium; 2003. p. 440-4.
33. Dolin R.H., Alschuler L., Beebe C., Biron P.V., Boyer S.L., Essin D., et al. The HL7 clinical document architecture. J Am Med Inform Assoc 2001, 8(6):552-569.
34. Chakraborty S, Blach C, Gardner M, McCourt B, Nahm M, Tenenbaum JD. The MURDOCK integrated data repository (MIDR): an integrative platform for biomarker research. In: Proceedings of AMIA summit for clinical research informatics. San Francisco, CA; 2010.
35. Fridsma D.B., Evans J., Hastak S., Mead C.N. The BRIDG project: a technical report. J Am Med Inform Assoc 2008, 15(2):130-137.
36. Han H, Wang W-Y, Mao B-H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang D-S, Zhang X-P, Huang G-B, editors. Advances in intelligent computing, vol. 3644. Springer Berlin/Heidelberg; 2005. p. 878-87.