Federated tensor factorization for computational phenotyping

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn Part F129685, Issue , 2017, Pages 887-895

  Kim, Yejin ^a,c   Sun, Jimeng ^b   Yu, Hwanjo ^a   Jiang, Xiaoqian ^c  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

ADMM; Federated approach; Phenotype

Indexed keywords

DATA MINING; FACTORIZATION; HOSPITALS; ITERATIVE METHODS;

ADMM; ALTERNATING DIRECTION METHOD OF MULTIPLIERS; EFFECTIVE APPROACHES; ELECTRONIC HEALTH RECORD; FEDERATED APPROACH; INSTITUTIONAL POLICIES; PHENOTYPE; TENSOR FACTORIZATION;

TENSORS;

EID: 85029054562     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3097983.3098118     Document Type: Conference Paper

References (28)

1. C. Bennett, T. Doub, and R. Selove. EHRs connect research and practice: Where predictive modeling, artificial intelligence, and clinical decision support intersect. Health Policy and Technology, 1(2): 105-114, June 2012.
2. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1): 1-122, 2011.
3. J. D. Carroll and J.-J. Chang. Analysis of individual differences in multidimensional scaling via an n-way generalization of "eckart-young" decomposition. Psychometrika, 35(3): 283-319, 1970.
4. R.J. Carroll, A. E. Eyler, and J. C. Denny. Naïve electronic health record phenotype identification for rheumatoid arthritis. Proceedings of the American Medical Informatics Association Annual Symposium, pages 189-196, Jan. 2011.
5. R. J. Carroll, W. K. Thompson, A. E. Eyler, A. M. Mandelin, T. Cai, R. M. Zink, J. A. Pacheco, C. S. Boomershine, T. A. Lasko, H. Xu, E. W. Karlson, R. G. Perez, V. S. Gainer, S. N. Murphy, E. M. Ruderman, R. M. Pope, R. M. Plenge, A. N. Kho, K. P. Liao, and J. C. Denny. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. Journal of the American Medical Informatics Association, 19(1e): e162-e169, June 2012.
6. K. Chaudhuri, C. Monteleoni, and A. Sarwate. Differentially Private Empirical Risk Minimization. Journal of Machine Learning Research (JMLR), 12: 1069-1109, July 2011.
7. J. H. Choi and S. Vishwanathan. Dfacto: Distributed factorization of tensors. In Advances in Neural Information Processing Systems, pages 1296-1304, 2014.
8. J. J. Cimino. From data to knowledge through concept-oriented terminologies: Experience with the Medical Entities Dictionary. Journal of the American Medical Informatics Association, 7(3): 288-297, May 2000.
9. A. L. De Almeida and A. Y. Kibangou. Distributed large-scale tensor decomposition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 26-30. IEEE, 2014.
10. C. Dwork. Differential privacy. International Colloquium on Automata, Languages and Programming, 4052(d): 1-12, 2006.
11. S. Ebadollahi, J. Sun, D. Gotz, J. Hu, D. Sow, and C. Neti. Predicting patient's trajectory of physiological data using temporal trends in similar patients: A system for near-term prognostics. AMIA Annual Symposium Proceedings, 2010: 192-196, 2010. PMID: 21346967.
12. S. Greengard. A new model for healthcare. Communications of the ACM, 56(2): 17-19, 2013.
13. J. C. Ho, J. Ghosh, S. R. Steinhubl, W. F. Stewart, J. C. Denny, B. A. Malin, and J. Sun. Limestone: High-throughput candidate phenotype generation via tensor factorization. Journal of biomedical informatics, 52: 199-211, 2014.
14. J. C. Ho, J. Ghosh, and J. Sun. Marble: High-throughput Phenotyping from Electronic Health Records via Sparse Nonnegative Tensor Factorization. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, pages 115-124, New York, NY, USA, 2014. ACM.
15. M. Hong, Z.-Q. Luo, and M. Razaviyayn. Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems. SIAM Journal on Optimization, 26(1): 337-364, 2016.
16. U. Kang, E. Papalexakis, A. Harpale, and C. Faloutsos. Gigatensor: scaling tensor analysis up by 100 times-algorithms and discoveries. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 316-324. ACM, 2012.
17. Y. Kim, J. Sun, H. Yu, and X. Jiang. Federated Tensor Factorization for Computational Phenotyping. http://dm.postech.ac.kr/trip, 2016.
18. L. Kissner and D. Song. Privacy-preserving set operations. In Advances in Cryptology-CRYPTO 2005, pages 241-257. Springer, 2005.
19. I. Kononenko. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine, 23(1): 89-109, Aug. 2001.
20. H. W. Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2): 83-97, 1955.
21. R. S. Ledley and L. B. Lusted. Reasoning foundations of medical diagnosis. M.D. computing: computers in medical practice, 8(5): 300-315, Sept. 1991.
22. Y. Li, X. Jiang, S. Wang, H. Xiong, and L. Ohno-Machado. Vertical grid logistic regression (vertigo). Journal of the American Medical Informatics Association, pageocv146, 2015.
23. Z. Lin, R. Liu, and Z. Su. Linearized alternating direction method with adaptive penalty for low-rank representation. In Advances in neural information processing systems, pages 612-620, 2011.
24. S. N. Murphy, G. Weber, M. Mendis, V. Gainer, H. C. Chueh, S. Churchill, and I. Kohane. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2): 124-130, 2010.
25. PCORnet Data Privacy Task Force. Technical Approaches for Protecting Privacy in the PCORnet Distributed Research, 2015.
26. M. Saeed, M. Villarroel, A. T Reisner, G. Clifford, L.-W Lehman, G. Moody, T Heldt, T H. Kyaw, B. Moody, and R. G. Mark. Multiparameter intelligent monitoring in intensive care ii (mimic-ii): A public-access intensive care unit database. Critical Care Medicine, 39: 952-960, May 2011.
27. J. Vaidya, H. Yu, and X. Jiang. Privacy-preserving svm classification. Knowledge and Information Systems, 14(2): 161-178, 2008.
28. Y. Wang, R. Chen, J. Ghosh, J. C. Denny, A. Kho, Y. Chen, B. A. Malin, and J. Sun. Rubik: Knowledge guided tensor factorization and completion for health data analytics. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1265-1274. ACM, 2015.