A framework for individualizing predictions of disease trajectories by exploiting multi-resolution structure

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 748-756

  Schulam, Peter ^a   Saria, Suchi ^a  

^a Johns Hopkins University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTINUOUS TIME SYSTEMS; FORECASTING; INFORMATION SCIENCE; LEARNING ALGORITHMS; POPULATION STATISTICS; PULMONARY DISEASES;

CONTINUOUS TIME FUNCTIONS; DIFFERENT RESOLUTIONS; INTERSTITIAL LUNG DISEASE; LATENT VARIABLE MODELING; PERSONALIZED MEDICINES; PREDICTIVE ACCURACY; RESOLUTION STRUCTURE; STATISTICAL STRENGTH;

TRAJECTORIES;

EID: 84965144591     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. J. Craig. Complex diseases: Research and applications. Nature Education, 1(1):184, 2008.
2. J. Varga, C. P. Denton, and F. M. Wigley. Scleroderma: From Pathogenesis to Comprehensive Management. Springer Science & Business Media, 2012.
3. J. Lötvall et al. Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome. Journal of Allergy and Clinical Immunology, 127(2):355-360, 2011.
4. L. D. Wiggins, D. L. Robins, L. B. Adamson, R. Bakeman, and C. C. Henrich. Support for a dimensional view of autism spectrum disorders in toddlers. Journal of autism and developmental disorders, 42(2):191-200, 2012.
5. P. J. Castaldi et al. Cluster analysis in the copdgene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema. Thorax, 2014.
6. S. Saria and A. Goldenberg. Subtyping: What Is It and Its Role in Precision Medicine. IEEE Intelligent Systems, 30, 2015.
7. D. S. Lee, P. C. Austin, J. L. Rouleau, P. P Liu, D. Naimark, and J. V. Tu. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. Jama, 290(19):2581-2587, 2003.
8. D. Rizopoulos. Dynamic predictions and prospective accuracy in joint models for longitudinal and timetoevent data. Biometrics, 67(3):819-829, 2011.
9. C. Proust-Lima et al. Joint latent class models for longitudinal and time-to-event data: A review. Statistical Methods in Medical Research, 23(1):74-90, 2014.
10. K. P. Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.
11. S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson, and S. Aigrain. Gaussian processes for timeseries modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1984):20110550, 2013.
12. J. Q. Shi, R. Murray-Smith, and D. M. Titterington. Hierarchical gaussian process mixtures for regression. Statistics and computing, 15(1):31-41, 2005.
13. A. Gelman and J. Hill. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, 2006.
14. H. Wang et al. High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer's disease progression prediction. In Advances in Neural Information Processing Systems, pages 1277-1285, 2012.
15. J. Ross and J. Dy. Nonparametric mixture of gaussian processes with constraints. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pages 1346-1354, 2013.
16. P. F. Schulam, F. M. Wigley, and S. Saria. Clustering longitudinal clinical marker trajectories from electronic health data: Applications to phenotyping and endotype discovery. In Proceedings of the Twinty-Ninth AAAI Conference on Artificial Intelligence, 2015.
17. B. M. Marlin. Modeling user rating profiles for collaborative filtering. In Advances in neural information processing systems, 2003.
18. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734-749, 2005.
19. D. Sontag, K. Collins-Thompson, P. N. Bennett, R. W. White, S. Dumais, and B. Billerbeck. Probabilistic models for personalizing web search. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 433-442. ACM, 2012.
20. C. E. Rasmussen and C. K. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006.
21. Y. Allanore et al. Systemic sclerosis. Nature Reviews Disease Primers, 2015.
22. D. Khanna et al. Clinical course of lung physiology in patients with scleroderma and interstitial lung disease: analysis of the scleroderma lung study placebo group. Arthritis & Rheumatism, 63(10):3078-3085, 2011.
23. J. Q. Shi, B. Wang, E. J. Will, and R. M. West. Mixed-effects gaussian process functional regression models with application to dose-response curve prediction. Stat. Med., 31(26):3165-3177, 2012.