Random forests classification analysis for the assessment of diagnostic skill

American Journal of Medical Quality

Volumn 25, Issue 2, 2010, Pages 149-153

  Katz, James D ^a   Mamyrova, Gulnara ^a   Guzhva, Olena ^a   Furmark, Lena ^a  

^a GEORGE WASHINGTON UNIVERSITY   (United States)

Author keywords

Assessment; Fibromyalgia; Medical education; Random forests

Indexed keywords

ALGORITHM; ARTICLE; CLINICAL COMPETENCE; FIBROMYALGIA; HEALTH CARE SURVEY; HUMAN; MEDICAL EDUCATION; STANDARD;

ALGORITHMS; CLINICAL COMPETENCE; FIBROMYALGIA; HEALTH CARE SURVEYS; HUMANS; INTERNSHIP AND RESIDENCY;

EID: 77949315508     PISSN: 10628606     EISSN: 1555824X     Source Type: Journal    
DOI: 10.1177/1062860609354639     Document Type: Article

References (23)

1. Huddle TS, Heudebert GR Taking apart the art: the risk of anatomizing clinical competence. Acad Med. 2007;82:536-541.
2. Merriam SB, Caffarella RS, Baumgartner LM Traditional Learning Theories in Learning in Adulthood. A Comprehensive Guide. 3rd ed. San Francisco, CA: Jossey-Bass; 2007.
3. Mezirow J. Understanding transformation theory. Adult Educ Q. 1994;44:222-232.
4. Kasl E., Marsick VJ, Dechant K. Teams as learners: a research-based model of team learning. J Appl Behav Sci. 1997;33:227-246.
5. Srinivasan M., Hauer KE, Der-Martirosian C., Wilkes M., Gesundheit N. Does feedback matter? Practice-based learning for medical students after a multi-institutional clinical performance examination. Med Educ. 2007;41:857-865.
6. Kroll L., Singleton A., Collier J., Jones IR Learning not to take it seriously: junior doctors' accounts of error. Med Educ. 2008;42:982-990.
7. Bradly LA, McKendree-Smith NL Central nervous system mechanisms of pain in fibromyalgia and other musculoskeletal disorders: behavioral and psychologic treatment approaches. Curr Opin Rheumatol. 2002;14:45-51.
8. Katz JD, Seaman R., Diamond S. Exposing gender bias in medical taxonomy: towards embracing a gender difference without disenfranchising women. Womens Health Issues. 2008;18:151-154.
9. Dadabhoy D., Clauw DJ Musculoskeletal signs and symptoms. In: Klippel JH, Stone JH, Crofford LJ, White PH, eds. Primer on the Rheumatic Disease. 13th ed. New York, NY: Springer Science; 2008:87-93.
10. Random Forests. Leo Brieman and Adele Cutler. http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.htm. Accessed November 2, 2009.
11. Zhang B., Zhang Y., Begg RK Gait classification in children with cerebral palsy by Bayesian approach. Pattern Recognit Comput Life Sci. 2009;42:581-586.
12. Brenning A. Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection. Remote Sensing Environ. 2009;113:239-247.
13. Strobl C., Boulesteix A., Kneib T., Augustin T., Zeileis A. Conditional variable importance for random forests. BMC Bioinformatics. 2008;9:307.
14. Breiman L. Random forests. Mach Learn. 2001;45:5-32.
15. Chang JS, Yeh R., Wiencke JK,et al.Pathway analysis of single-nucleotide polymorphisms potentially associated with glioblastoma multiforme susceptibility using random forests. Cancer Epidemiol Biomarkers Prev. 2008;17:1368-1373.
16. Harb R., Yan X., Radwan E., Su X. Exploring precrash maneuvers using classification trees and random forests. Accid Anal Prev. 2009;41:98-107.
17. Cox M., Irby DM Assessment in medical education. N Engl J Med. 2007;356:387-396.
18. Pratt DD, Arseneau R., Collins JB Reconsidering good teaching across the continuum of medical education. J Contin Educ Health Prof. 2001;21:70-81.
19. Ward MM, Pajevic S., Dreyfuss J., Malley JD Short-term prediction of mortality in patients with systemic lupus erythematosus: classification of outcomes using random forests. Arthritis Rheum. 2006;55:74-80.
20. Bhattacharyya S., Epstein J., Suva LJ Biomarkers that discriminate multiple myeloma patients with or without skeletal involvement detected using SELDI-TOF mass spectrometry and statistical and machine learning tools. Dis Markers. 2006;22:245-255.
21. Perdiguero-Alonso D., Montero FE, Kostadinova A., Raga JA, Barrett J. Random forests, a novel approach for discrimination of fish populations using parasites as biological tags. Int J Parasitol. 2008;38:1425-1434.
22. Chan JC, Paelinckx D. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyper-spectral imagery. Remote Sensing Environ. 2008;112: 2999-3011.
23. Mamyrova G., O'Hanlon TP, Sillers L., Malley K., James-Newton L., Parks C., Cooper G., Pandey JP, Miller FW, Rider LG, for the Childhood Myositis Heterogeneity Collaborative Study Group. Cytokine gene polymorphisms as risk and severity factors for juvenile dermatomyositis. Arthritis and Rheum. 2008; 58:3941-50.