Removing confounding factors via constraint-based clustering: An application to finding homogeneous groups of multiple sclerosis patients

Artificial Intelligence in Medicine

Volumn 65, Issue 2, 2015, Pages 79-88

  Liu, Jingjing ^a   Brodley, Carla E ^b   Healy, Brian C ^c   Chitnis, Tanuja ^d  

^a Tufts University   (United States)

^b Northeastern University   (United States)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

^d BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Confounding factor; Constraint based clustering; Mining medical data; Multiple sclerosis; Physician subjectivity

Indexed keywords

AUDITION; BINS; HOSPITAL DATA PROCESSING; MEDICAL COMPUTING; PATIENT TREATMENT;

CONFOUNDING FACTOR; CONSTRAINT-BASED CLUSTERING; MEDICAL DATA; MULTIPLE SCLEROSIS; PHYSICIAN SUBJECTIVITY;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; CLUSTER ANALYSIS; CONFOUNDING VARIABLE; DISEASE COURSE; HUMAN; MATHEMATICAL ANALYSIS; MULTIPLE SCLEROSIS; PRIORITY JOURNAL; EPIDEMIOLOGY;

CLUSTER ANALYSIS; CONFOUNDING FACTORS (EPIDEMIOLOGY); HUMANS; MULTIPLE SCLEROSIS;

EID: 84943581966     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2015.06.004     Document Type: Article

References (46)

1. Cho M., Washko G., Hoffmann T., Criner G., Hoffman E., Martinez F., et al. Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation. Respir Res 2010, 30-38.
2. Paoletti M., Camiciottoli G., Meoni E., Bigazzi F., Cestelli L., Pistolesi M., et al. Explorative data analysis techniques and unsupervised clustering methods to support clinical assessment of COPD phenotypes. J Biomed Inform 2009, 1013-1021.
3. Constrained clustering: algorithms, applications and theory 2008, Chapman & Hall/CRC. S. Basu, I. Davidson, K. Wagstaff (Eds.).
4. Compston A., McDonald I., Noseworthy J., Lassmann H., Vienna V., Miller D., et al. McAlpine's multiple sclerosis 2005, Churchill Livingstone. 4th ed.
5. Ramsaransing G.S., Keyser J.D. Benign course in multiple sclerosis: a review. Acta Neurol Scand 2006, 113(6):359-369.
6. Healy B., Ali E., Guttmann C., Chitnis T., Glanz B., Buckle G., et al. Smoking and disease progression in multiple sclerosis. Arch Neurol 2009, 858-864.
7. Gauthier S., Glanz B., Mandel M., Weiner H. A model for the comprehensive investigation of a chronic autoimmune disease: the multiple sclerosis CLIMB study. Autoimmun Rev 2006, 5(8):532-536.
8. Kurtzke J. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983, 33(11):1444-1452.
9. Vestbo J., Hurd S., Agusti A., Jones P., Vogelmeier C., Anzueto A. Global strategy for the diagnosis, management, and prevention of COPD: GOLD executive summary. Am J Respir Crit Care Med 2007, 532-555.
10. Criner G., Cordova F., Sternberg A., Martinez F. National emphysema treatment trial. Proc Am Thorac Soc 2008, 393-405.
11. Yanosky D.J. Comparability of covariance structures and accuracy of information criteria in mixed model methods for longitudinal data analysis 2007, The University of Georgia, PhD thesis.
12. Gondek D., Hofmann T. Conditional information bottleneck clustering. International conference on data mining, workshop on clustering large data sets 2003, 36-42. IEEE Computer Society, Los Alamitos, CA, USA.
13. Bae E., Bailey J. COALA: a novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. International conference on data mining 2006, 53-62. IEEE Computer Society, Los Alamitos, CA, USA.
14. Gondek D., Hoffmann T. Non-redundant clustering with conditional ensembles. International conference on knowledge discovery and data mining 2005, 70-77. ACM, New York, USA.
15. Tishby N., Pereira F.C., Bialek W. The information bottleneck method. Allerton conference on communication, control and computing 1999, 368-377.
16. Strehl A., Ghosh J. Cluster ensembles: a knowledge reuse framework for combining partitionings. J Mach Learn Res 2003, 583-617.
17. Biba M., Esposito F., Ferilli S., Di Mauro N., Basile T. Unsupervised discretization using kernel density estimation. International joint conference on artificial intelligence 2007, 696-701. AAAI Press.
18. Chiu D., Wong A., Cheung B. Information discovery through hierarchical maximum entropy discretization and synthesis. International conference on knowledge discovery and data mining 1991, 125-140. ACM, New York, USA.
19. Dempster A., Laird N., Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 1977, 1-38.
20. Schwarz G. Estimating the dimension of a model. Ann Stat 1978, 461-464.
21. Preston D., Brodley C., Khardon R., Sulla-Menashe D., Friedl M. Redefining class definitions using constraint-based clustering: an application to remote sensing of the Earth's surface. International conference on knowledge discovery and data mining 2010, 823-832. ACM, New York, USA.
22. Lu Z., Leen T.K. Penalized probabilistic clustering. Neural Comput 2007, 19(6):1528-1567.
23. Wagstaff K., Cardie C., Rogers S., Schroedl S. Constrained k-means clustering with background knowledge. International conference of machine learning 2001, 577-584. Morgan Kaufmann Publishers Inc.
24. Basu S. A probabilistic framework for semi-supervised clustering. International conference on knowledge discovery and data mining 2004, 59-68. ACM, New York, USA.
25. Shental N., Bar-Hillel A., Hertz T., Weinshall D. Computing Gaussian mixture models with EM using equivalence constraints. Advances in neural information processing systems 2004, 465-472. MIT Press, Cambridge, MA, USA.
26. Xing E.P., Ng A.Y., Jordan M.I., Russell S. Distance metric learning, with application to clustering with side-information. Advances in neural information processing systems 2002, 505-512. MIT Press, Cambridge, MA, USA.
27. Lu Z., Carreira-Perpinan M.A. Constrained spectral clustering through affinity propagation. Computer society conference on computer vision and pattern recognition 2008, 1-8. IEEE Computer Society, Los Alamitos, CA, USA.
28. Kulis B., Basu S., Dhillon I., Mooney R.J. Semi-supervised graph clustering: a kernel approach. International conference on machine learning 2005, 457-464. Springer.
29. Davidson I., Ravi S.S. Agglomerative hierarchical clustering with constraints: theoretical and empirical results. European conference on principles and practice of knowledge discovery in databases 2005, 59-70. Springer, Berlin, Heidelberg.
30. Li A., Walling J., Ahn S., Kotliarov Y., Su Q., Quezado M., et al. Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes. Cancer Res 2009, 69(5):2091-2099.
31. Jiao L.C., Shang F., Wang F., Liu Y. Fast semi-supervised clustering with enhanced spectral embedding. Pattern Recognit 2012, 45(12):4358-4369.
32. Ji X., Xu W. Document clustering with prior knowledge. International conference on research and development in information retrieval 2006, 405-412. ACM, New York, USA.
33. Lu Z., Carreira-Perpinán M.A. Constrained spectral clustering through affinity propagation. Conference on computer vision and pattern recognition 2008, 1-8. IEEE Computer Society, Los Alamitos, CA, USA.
34. Xu Q., Desjardins M. Constrained spectral clustering under a local proximity structure assumption. International conference of the Florida artificial intelligence research society 2005, 866-867. IAAA Press, Menlo Park, CA.
35. Kullback S. Information theory and statistics 1997, Dover.
36. Lichman M. UCI machine learning repository 2013, URL http://archive.ics.uci.edu/ml.
37. Chatterjee S., Hadi A. Influential observations, high leverage points, and outliers in linear regression. Statistical science 1986, 379-436. Institute of Mathematical Statistics.
38. Hohol M., Orav E., Weiner H. Disease steps in multiple sclerosis: a simple approach to evaluate disease progression. Neurology 1995, 45(2):251-255.
39. Hobart J., Freeman J., Thompson A. Kurtzke scales revisited: the application of psycometric methods to clinical intuition. Brain 2000, 123(5):1027-1040.
40. D'Souza M., Yaldizli O., Lucassen E., Kornyeyeva E., Kappos L. The effect of standardised EDSS re-training on the performance of EDSS raters. Mult Scler J 2012, Sage Publications, pp. 18:339.
41. Parsons L., Haque E., Liu H. Subspace clustering for high dimensional data: a review. SIGKDD Explor 2004, 73:90-105.
42. Gholipour T., Healy B., Baruch N., Weiner H., Chitnis T. Demographic and clinical characteristics of malignant multiple sclerosis. Neurology 2011, 76(23):1996-2001.
43. Celius E., Harbo H., Egeland T., Vartdal F., Vandvik B., Spurkiand A. Sex and age at diagnosis are correlated with the hla-dr2 dq6 haplotype in multiple sclerosis. J Neurol Sci 2000, 178(2):132-135.
44. Hensiek A., Sawcer S., Feakes R., Deans J., Mander A., Akesson E., et al. HLA-DR 15 is associated with female sex and younger age at diagnosis in multiple sclerosis. J Neurol Neurosurg Psychiatry 2002, 72(2):184-187.
45. Masterman T., Ligers A., Olsson T., Andersson M., Olerup O., Hillert J. HLA-DR15 is associated with lower age at onset in multiple sclerosis. Ann Neurol 2000, 48(2):211-219.
46. Al-Shammari S., Nelson R.F., Voevodin A. HHV-6 DNAaemia in patients with multiple sclerosis in Kuwait. Acta Neurol Scand 2003, 107:122-124.