Constructing disease network and temporal progression model via context-sensitive hawkes process

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn 2016-January, Issue , 2016, Pages 721-726

  Choi, Edward ^a   Du, Nan ^a   Chen, Robert ^a   Song, Le ^a   Sun, Jimeng ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

Disease Prediction; Disease Relation; EHR; Hawkes Process; Health Analytics; Point Process

Indexed keywords

HEALTH; PREDICTIVE ANALYTICS;

CONTEXT SENSITIVE; ELECTRONIC HEALTH RECORD; PATIENT INFORMATION; POINT PROCESS; PREDICTIVE PERFORMANCE; PUBLIC HEALTH POLICIES; RELATIONSHIP NETWORKS; TECHNICAL CHALLENGES;

DATA MINING;

EID: 84963511141     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2015.144     Document Type: Conference Paper

References (25)

1. L. Ohno-Machado, "Nih's big data to knowledge initiative and the advancement of biomedical informatics," Journal of the American Medical Informatics Association, vol. 21, no. 2, pp. 193-193, 2014
2. F. Wang, P. Zhang, B. Qian, X. Wang, and I. Davidson, "Clinical risk prediction with multilinear sparse logistic regression," in KDD, 2014, pp. 145-154
3. J. Zhou, F. Wang, J. Hu, and J. Ye, "From micro to macro: Data driven phenotyping by densification of longitudinal electronic medical records," in KDD, 2014, pp. 135-144
4. Y. Park and J. Ghosh, "Ludia: An aggregate-constrained low-rank reconstruction algorithm to leverage publicly released health data," in KDD, 2014, pp. 55-64
5. J. C. Ho, J. Ghosh, and J. Sun, "Marble: High-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization," in KDD, 2014, pp. 115-124
6. M. Ghassemi, T. Naumann, F. Doshi-Velez, N. Brimmer, R. Joshi, A. Rumshisky, and P. Szolovits, "Unfolding physiological state: Mortality modelling in intensive care units," in KDD, 2014, pp. 75-84
7. X. Wang, D. Sontag, and F. Wang, "Unsupervised learning of disease progression models," in KDD, 2014, pp. 85-94
8. A. G. Hawkes and D. Oakes, "A cluster process representation of a selfexciting process," Journal of Applied Probability, pp. 493-503, 1974
9. J. Leiva-Murillo, A. Artés-Rodríguez, and E. Baca-García, "Visualization and prediction of disease interactions with continuous-time hidden markov models," in NIPS 2011 Workshop on Personalized Medicine, 2011
10. G. Savova, S. Bethard, W. Styler, J. Martin, M. Palmer, J. Masanz, and W. Ward, "Towards temporal relation discovery from the clinical narrative," in AMIA, 2009, pp. 568-572
11. Y. Zhao, X. Qi, Z. Liu, Y. Zhang, and T. Zheng, "Mining medical records with a klipi multi-dimensional hawkes model," in KDD 2014 Workshop on Health Informatics, 2014
12. J. Zhou, J. Liu, V. A. Narayan, and J. Ye, "Modeling disease progression via fused sparse group lasso," in KDD, 2012, pp. 1095-1103
13. Y.-Y. Liu, H. Ishikawa, M. Chen, G. Wollstein, J. S. Schuman, and J. M. Rehg, "Longitudinal modeling of glaucoma progression using 2-dimensional continuous-time hidden markov model," in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013, 2013, pp. 444-451
14. S. Myers and J. Leskovec, "On the convexity of latent social network inference," in NIPS, 2010, pp. 1741-1749
15. M. Gomez Rodriguez, J. Leskovec, and A. Krause, "Inferring networks of diffusion and influence," in KDD. ACM, 2010, pp. 1019-1028
16. M. Gomez Rodriguez, D. Balduzzi, B. Schölkopf, G. T. Scheffer et al., "Uncovering the temporal dynamics of diffusion networks," in ICML, 2011, pp. 561-568
17. N. Du, L. Song, M. Yuan, and A. J. Smola, "Learning networks of heterogeneous influence," in NIPS, 2012, pp. 2789-2797
18. L. Wang, S. Ermon, and J. E. Hopcroft, "Feature-enhanced probabilistic models for diffusion network inference," in ECML PKDD, 2012, pp. 499-514
19. N. Du, L. Song, H. Woo, and H. Zha, "Uncover topic-sensitive information diffusion networks," in AISTATS, 2013, pp. 229-237
20. M. Saeed et al., "Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database," Critical care medicine, vol. 39, no. 5, p. 952, 2011
21. S. J. Kernis, K. J. Harjai, G. W. Stone, L. L. Grines, J. A. Boura, M. W. Yerkey, W. O'Neill, and C. L. Grines, "The incidence, predictors, and outcomes of early reinfarction after primary angioplasty for acute myocardial infarction," Journal of the American College of Cardiology, vol. 42, no. 7, pp. 1173-1177, 2003
22. P. S. Jhund and J. J. McMurray, "Heart failure after acute myocardial infarction a lost battle in the war on heart failure?" Circulation, vol. 118, no. 20, pp. 2019-2021, 2008
23. E. F. Goljan, Rapid Review Pathology, 12nd ed. Elsevier Saunders, 2014
24. R. Chapman, Z. Varghese, R. Gaul, G. Patel, N. Kokinon, and S. Sherlock, "Association of primary sclerosing cholangitis with hla-b8." Gut, vol. 24, no. 1, pp. 38-41, 1983
25. M. A. Goldstein, The MassGeneral Hospital for Children Adolescent Medicine Handbook. Springer Science &Business Media, 2010