Ontology-based temporal relation modeling with map-reduce latent dirichlet allocations for big EHR data

Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012

Volumn , Issue , 2012, Pages 708-715

  Li, Dingcheng ^a   Tao, Cui ^a   Liu, Hongfang ^a   Chute, Christopher ^a  

^a MAYO CLINIC   (United States)

Author keywords

event coreference resolution; latent Dirichlet allocations; MapReduce; temporal relation annotation

Indexed keywords

AUTOMATIC ANNOTATION; BIG DATUM; BIG SIZES; CLINICAL NOTES; CO-REFERENCE RESOLUTIONS; DATA SPARSENESS; ELECTRONIC HEALTH RECORD; HIGH DIMENSIONALITY; LATENT DIRICHLET ALLOCATION; LATENT DIRICHLET ALLOCATIONS; MAP-REDUCE; MAPREDUCE FRAMEWORKS; NON-PARAMETRIC BAYESIAN; ONTOLOGY-BASED; SEQUENTIAL MODELING; SIDE INFORMATION; TEMPORAL RELATION; TIME EVENT; TIME-STAMP; TOPIC MODELING; VARIATIONAL METHODS;

BAYESIAN NETWORKS; HIDDEN MARKOV MODELS;

PARAMETER ESTIMATION;

EID: 84874602478     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CGC.2012.112     Document Type: Conference Paper

References (26)

1. B. Meinow, I. Kåreholt, M. Parker, and M. Thorslund, "The effect of the duration of follow-up in mortality analysis: The temporal pattern of different predictors," The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, vol. 59, no. 3, pp. S181-S189, 2004. (Pubitemid 38585870)
2. P. Hessburg, E. Kuhlmann, and T. Swetnam, "Examining the recent climate through the lens of ecology: inferences from temporal pattern analysis," Ecological Applications, vol. 15, no. 2, pp. 440-457, 2005. (Pubitemid 40441288)
3. E. Saad, D. Prokhorov, D. Wunsch et al., "Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks," Neural Networks, IEEE Transactions on, vol. 9, no. 6, pp. 1456-1470, 1998. (Pubitemid 128742422)
4. Z. Xiang, R. Minter, X. Bi, P. Woolf, and Y. He, "minituba: medical inference by network integration of temporal data using bayesian analysis," Bioinformatics, vol. 23, no. 18, pp. 2423-2432, 2007. (Pubitemid 47423840)
5. F. Chen, X. Ding, Y. Ding, Z. Xiang, X. Li, D. Ghosh, G. Schurig, N. Sriranganathan, S. Boyle, and Y. He, "Proinflammatory caspase-2-mediated macrophage cell death induced by a rough attenuated brucella suis strain," Infection and immunity, vol. 79, no. 6, pp. 2460-2469, 2011.
6. A. Borrie, G. Jonsson, and M. Magnusson, "Temporal pattern analysis and its applicability in sport: an explanation and exemplar data," Journal of Sports Sciences, vol. 20, no. 10, pp. 845-852, 2002.
7. I. Witten, E. Frank, and M. Hall, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2011.
8. K. Clark, D. Sharma, C. Chute, and C. Tao, "Application of a temporal reasoning framework tool in analysis of medical device adverse events," in AMIA Annual Symposium Proceedings, vol. 2011. American Medical Informatics Association, 2011, p. 1366.
9. D. Blei and J. McAuliffe, "Supervised topic models," Advances in Neural Information Processing Systems, vol. 20, pp. 121-128, 2008.
10. T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum, "Integrating topics and syntax," Advances in neural information processing systems, vol. 17, pp. 537-544, 2005.
11. G. Savova, J. Masanz, P. Ogren, J. Zheng, S. Sohn, K. Kipper-Schuler, and C. Chute, "Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications," Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 507-513, 2010.
12. D. Blei, A. Ng, and M. Jordan, "Latent dirichlet allocation," The Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
13. Y. Wang, H. Bai, M. Stanton, W. Chen, and E. Chang, "Plda: Parallel latent dirichlet allocation for large-scale applications," Algorithmic Aspects in Information and Management, pp. 301-314, 2009.
14. T. Griffiths and Z. Ghahramani, "Infinite latent feature models and the indian buffet process," 2005.
15. J. Cai, W. Lee, and Y. Teh, "Improving word sense disambiguation using topic features," in Proc. of EMNLP, 2007.
16. W. Li and A. McCallum, "Semi-supervised sequence modeling with syntactic topic models," in PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, vol. 20, no. 2. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2005, p. 813.
17. A. Haghighi and D. Klein, "Unsupervised coreference resolution in a nonparametric bayesian model," in ANNUAL MEETING-ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, vol. 45, no. 1, 2007, p. 848.
18. -, "Coreference resolution in a modular, entity-centered model," in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2010, pp. 385-393.
19. X. Wang and A. McCallum, "Topics over time: a non-markov continuous-time model of topical trends," in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006, pp. 424-433.
20. X. Wang, N. Mohanty, and A. McCallum, "Group and topic discovery from relations and text," in Proceedings of the 3rd international workshop on Link discovery. ACM, 2005, pp. 28-35.
21. D. Li, S. Somasundaran, and A. Chakraborty, "A combination of topic models with max-margin learning for relation detection," ACL HLT 2011, p. 1, 2011.
22. D. Li, S. Somasundaran, and A. Charkraborty, "Erdmedlda: Entity relation detection using supervised topic models with maximum margin learning," Natural Language Engineering, vol. 18, no. 2, p. 263, 2012.
23. J. Zhu, A. Ahmed, and E. Xing, "MedLDA: A General Framework of Maximum Margin Supervised Topic Models," stat, vol. 1050, p. 30, 2009.
24. -, "MedLDA: maximum margin supervised topic models for regression and classification," in Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009, pp. 1257-1264.
25. M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul, "An introduction to variational methods for graphical models," Machine learning, vol. 37, no. 2, pp. 183-233, 1999. (Pubitemid 30544678)
26. K. Zhai, J. Boyd-Graber, and N. Asadi, "Using variational inference and mapreduce to scale topic modeling," Arxiv preprint arXiv:1107.3765, 2011.