Time series analysis of nursing notes for mortality prediction via a state transition topic model

International Conference on Information and Knowledge Management, Proceedings

Volumn 19-23-Oct-2015, Issue , 2015, Pages 1171-1180

  Jo, Yohan ^a   Loghmanpour, Natasha ^a   Rosé, Carolyn Penstein ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Healthcare; Hidden Markov model; Latent dirichlet allocation; Medical data mining; Mortality prediction; Nursing notes; State transition topic model

Indexed keywords

DATA MINING; FORECASTING; HEALTH CARE; HIDDEN MARKOV MODELS; INTENSIVE CARE UNITS; KNOWLEDGE MANAGEMENT; MARKOV PROCESSES; MEDICAL COMPUTING; NURSING; STATISTICS;

CRITICAL CONDITION; LATENT DIRICHLET ALLOCATION; MEDICAL DATA MINING; SHORT TERM PREDICTION; STRONG CORRELATION; TEMPORAL INFORMATION; TIME SERIES MODELING; TOPIC MODELING;

TIME SERIES ANALYSIS;

EID: 84958246551     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2806416.2806541     Document Type: Conference Paper

References (26)

1. J. R. Beardsley, C. M. Jones, J. Chou, M. Currie-Coyoy, T. Jackson, and A. Orsborn. Code Sepsis: Improving Sepsis Care; Saving Patients' Lives. ASHP Best Practices Award, 2014.
2. D. M. Blei and J. D. Lafferty. Dynamic topic models. In ICML '06, pages 113-120, 2006.
3. D. M. Blei and P. J. Moreno. Topic segmentation with an aspect hidden Markov model. In ACM SIGIR '01, pages 343-348, 2001.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993-1022, 2003.
5. C.-c. Chia, Z. Syed, and A. Arbor. Scalable Noise Mining in Long-Term Electrocardiographic Time-Series to Predict Death Following Heart Attacks. KDD '14, pages 125-134, 2014.
6. M. Cvach. Monitor alarm fatigue: an integrative review. Biomedical instrumentation & technology, 46(4):268-77, 2012.
7. Z. J. Gao, Y. Song, S. Liu, H. Wang, H. Wei, Y. Chen, and W. Cui. Tracking and Connecting Topics via Incremental Hierarchical Dirichlet Processes. In IEEE ICDM '11, pages 1056-1061, 2011.
8. 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. KDD '14, pages 75-84, 2014.
9. A. Gruber, M. Rosen-Zvi, and Y. Weiss. Hidden topic Markov models. AISTATS '07, pages 163-170, 2007.
10. L. Hong, B. Dom, S. Gurumurthy, and K. Tsioutsiouliklis. A time-dependent topic model for multiple text streams. In KDD '11, page 832, 2011.
11. L. Hong, D. Yin, J. Guo, and B. D. Davison. Tracking trends: incorporating term volume into temporal topic models. In KDD '11, page 484, 2011.
12. A. Kalogeratos, V. Chasanis, G. Rakocevic, A. Likas, Z. Babovic, and M. Novakovic. Mining Clinical Data. In G. Rakocevic, T. Djukic, N. Filipovic, and V. Milutinović, editors, Computational Medicine in Data Mining and Modeling SE - 1, pages 1-34. 2013.
13. L.-W. Lehman, R. Adams, L. Mayaud, G. Moody, A. Malhotra, R. Mark, and S. Nemati. A physiological time series dynamics-based approach to patient monitoring and outcome prediction. IEEE journal of biomedical and health informatics, PP(99):1, 2014.
14. L.-w. Lehman, M. Saeed, W. Long, J. Lee, and R. Mark. Risk stratification of ICU patients using topic models inferred from unstructured progress notes. AMIA Annual Symposium proceedings, 2012:505-11, 2012.
15. Q. Mei and C. Zhai. Discovering evolutionary theme patterns from text. In KDD '05, page 198, 2005.
16. R. Menéndez, R. Martínez, S. Reyes, J. Mensa, X. Filella, M. A. Marcos, A. Martínez, C. Esquinas, P. Ramirez, and A. Torres. Biomarkers improve mortality prediction by prognostic scales in community-acquired pneumonia. Thorax, 64(7):587-91, 2009.
17. M. J. Paul. Mixed Membership Markov Models for Unsupervised Conversation Modeling. In EMNLP-CoNLL '12, pages 94-104, 2012.
18. Z. Syed, B. M. Scirica, S. Mohanavelu, P. Sung, E. L. Michelson, C. P. Cannon, P. H. Stone, C. M. Stultz, and J. V. Guttag. Relation of death within 90 days of non-ST-elevation acute coronary syndromes to variability in electrocardiographic morphology. The American journal of cardiology, 103(3):307-11, 2009.
19. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical Dirichlet Processes. Journal of the American Statistical Association, 101(476):1566-1581, 2006.
20. H. Wang, D. Zhang, and C. Zhai. Structural Topic Model for Latent Topical Structure Analysis. ACL '11, pages 1526-1535, 2011.
21. X. Wang and A. McCallum. Topics over time: a non-Markov continuous-time model of topical trends. In KDD '06, page 424, 2006.
22. Y. Wang, E. Agichtein, and M. Benzi. Tm-lda: Efficient online modeling of latent topic transitions in social media. In KDD '12, pages 123-131, 2012.
23. J. Wiens, E. Horvitz, and J. V. Guttag. Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. In Advances in Neural Information Processing Systems, pages 467-475, 2012.
24. J. Yang, J. McAuley, J. Leskovec, P. LePendu, and N. Shah. Finding progression stages in time-evolving event sequences. In WWW '14, pages 783-794, 2014.
25. J. Zhang, Y. Song, C. Zhang, and S. Liu. Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora. In KDD '10, page 1079, 2010.
26. J. Zhu and E. P. Xing. Conditional topic random fields. ICML '10, pages 1239-1246, 2010.