VERTIcal Grid lOgistic regression (VERTIGO)

Journal of the American Medical Informatics Association

Volumn 23, Issue 3, 2016, Pages 570-579

  Li, Yong ^a   Jiang, Xiaoqian ^b   Wang, Shuang ^b   Xiong, Hongkai ^a   Ohno Machado, Lucila ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Dual optimization; Federated data analysis; Logistic regression; Vertically partitioned data

Indexed keywords

ANIMAL MODEL; CALIBRATION; CLASSIFICATION; DATA ANALYSIS; DEATH; DEPENDENT VARIABLE; DISEASE MODEL; HUMAN; LOGISTIC REGRESSION ANALYSIS; MEDICAL RECORD; MEMORY; OPERATION DURATION; PRIVACY; RECEIVER OPERATING CHARACTERISTIC; SCALE UP; ALGORITHM; AREA UNDER THE CURVE; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; AREA UNDER CURVE; DATA INTERPRETATION, STATISTICAL; LOGISTIC MODELS; ROC CURVE;

EID: 84978971102     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1093/jamia/ocv146     Document Type: Article

References (28)

1. Ohno-Machado L, Agha Z, Bell DS, et al. pSCANNER: patient-centered Scalable National Network for Effectiveness Research. J Am Med Inform Assoc 2014;21(4):621-626. doi:10.1136/amiajnl-2014-002751.
2. Karr AF, Lin X, Sanil AP, et al. Secure regression on distributed databases. J Comput Graph Stat 2005;14:263-79. doi:10.1198/106186005X47714.
3. Fienberg S, Fulp W, Slavkovic A, et al. Secure log-linear and logistic regression analysis of distributed databases. In: PSD '06 Proceedings of the 2006 CENEX-SDC Project International Conference on Privacy in Statistical Databases. Berlin: Springer-Verlag; 2006:277-290.
4. Yu H, Jiang X, Vaidya J. Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. Adv Knowl Discov Data 2006;3918:647-56.
5. Ghosh J, Reiter JP, Karr AF. Secure computation with horizontally partitioned data using adaptive regression splines. Comput Stat Data Anal 2007;51(12):5813-5820.
6. Slavkovic AB, Nardi Y, Tibbits MM. "Secure" logistic regression of horizontally and vertically partitioned distributed databases. In: ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops. Washington, DC: IEEE Computer Society; 2007:723-728.
7. Kantarcioglu M. A survey of privacy-preserving methods across horizontally partitioned data. In: Aggarwal CC, Yu PS, eds. Privacy-Preserving Data Mining. Vol 34. New York, NY: Springer US; 2008:313-335.
8. Wu Y, Jiang X, Kim J, et al. Grid Binary LOgistic REgression (GLORE): building shared models without sharing data. J Am Med Inform Assoc 2012;19(5):758-764.
9. Yi X, Zhang Y. Privacy-preserving naive Bayes classification on distributed data via semi-trusted mixers. Inf Syst 2009;34(3):371-380.
10. Sanil AP, Karr AF, Lin X, et al. Privacy preserving regression modelling via distributed computation. In: KDD '04 Proceedings of the tenth ACM ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY: ACM; 2004:677. doi:10.1145/1014052.1014139.
11. Karr AF, Lin X, Sanil AP, et al. Privacy-preserving analysis of vertically partitioned data using secure matrix products. J Off Stat 2009;25(1):125-138.
12. Vaidya J, Kantarcioglu M, Clifton C. Privacy-preserving Naïve Bayes classification. VLDB J 2008;17(4):879-898.
13. Vaidya J. A survey of privacy-preserving methods across. Privacy- Preserving Data Min 2008;34:337-358.
14. Nardi Y, Fienberg SE, Hall RJ. Achieving both valid and secure logistic regression analysis on aggregated data from different private sources. J Priv Confidentiality 2012;4(1):9.
15. Yu H, Vaidya J, Jiang X. Privacy-preserving SVM classification on vertically partitioned data. In: PAKDD '06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining. Berlin: Springer-Verlag; 2006:47-56. doi:10.1007/11731139_74.
16. Minka TP. A comparison of numerical optimizers for logistic regression [technical report]. http://research.microsoft.com/en-us/um/people/minka/ papers/logreg/minka-logreg.pdf. Published 22 Oct 2003. Revised 26 Mar 2007 Accessed April 7, 2015.
17. CRAN-Package glmnet Website. http://cran.r-project.org/web/packages/ glmnet/index.html. Accessed April 7, 2015.
18. Jaakkola TS, Haussler D. Probabilistic Kernel Regression Models. In: The Seventh International Workshop on Artificial Intelligence and Statistics. San Francisco, CA: Morgan Kaufmann Publishers, Inc; 1999.
19. Hanley A, Mcneil J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiol 1982;143:29-36.
20. Hosmer DW, Hosmer T, Le Cessie S, et al. A comparison of goodness-offit tests for the Logistic Regression model. Stat Med 1997;16(9):965-980.
21. Duda RO, Hart PE, Stork DG. Pattern Classification. New York: John Wiley & Sons, Inc; 2012.
22. LAPACK-Linear Algebra PACKage Website. http://www.netlib.org/lapack/ #_software. Accessed January 30, 2015.
23. Keerthi SS, Duan KB, Shevade SK, et al. A fast dual algorithm for kernel logistic regression. Mach Learn. 2005;61(1-3):151-65. doi:10.1007/s10994-005- 0768-5.
24. GEO Accession viewer. National Center for Biotechnology Information Website. http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3494. Accessed January 30, 2015.
25. Osl M, Dreiseitl S, Kim J, et al. Effect of data combination on predictive modeling: a study using gene expression data. AMIA Annu Symp Proc 2010:567-571.
26. Kennedy RL, Burton AM, Fraser HS, et al. Early diagnosis of acute myocardial infarction using clinical and electrocardiographic data at presentation: derivation and evaluation of logistic regression models. Eur Heart J 1996;17(8): 1181-1191.
27. Saeed M, Villarroel M, Reisner AT, et al. Multiparameter intelligent monitoring in intensive care II: a public-access intensive care unit database. Crit Care Med 2011;39(5):952-60. doi:10.1097/CCM.0b013e31820a92c6.
28. Rockafellar RT. Convex Analysis. Princeton, NJ: Princeton University Press; 1970.