Disease-Atlas: Navigating Disease Trajectories using Deep Learning

Proceedings of Machine Learning Research

Volumn 85, Issue , 2018, Pages 137-160

  Lim, Bryan ^a   van der Schaar, Mihaela ^a,b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b ALAN TURING INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP NEURAL NETWORKS;

CYSTIC FIBROSIS; HIGH-DIMENSIONAL DATASET; INHERENT FLEXIBILITY; JOINT MODELS; LEARNING APPROACH; LONGITUDINAL DATA; LONGITUDINAL STUDY; MODEL SPECIFICATIONS; PERFORMANCE AND SCALABILITIES; TIME TO EVENTS;

SCALABILITY;

EID: 85160722598     PISSN: None     EISSN: 26403498     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (40)

1. Kartik Ahuja, William Zame, and Mihaela van der Schaar. Dpscreen: Dynamic personalized screening. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, NIPS 2017, pages 1321-1332. 2017.
2. A. M. Alaa and M. van der Schaar. Deep multi-task gaussian processes for survival analysis with competing risks. In Advances in Neural Information Processing Systems, NIPS 2017. 2017.
3. J Barrett and L. Su. Dynamic predictions using flexible joint models of longitudinal and timetoevent data. Statistics in Medicine., 36(9):1447-1460, April 2017.
4. Jessica Barrett, Peter Diggle, Robin Henderson, and David Taylor-Robinson. Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 77(1):131-148, 2015.
5. Douglas Bates, Martin Mächler, Ben Bolker, and Steve Walker. Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1):1-48, 2015. doi: 10.18637/jss. v067.i01.
6. Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. Doctor ai: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference, volume 56 of Proceedings of Machine Learning Research, pages 301-318, 2016.
7. Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pages 160-167, 2008.
8. Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song. Recurrent marked temporal point processes: Embedding event history to vector. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'16, pages 1555-1564, 2016.
9. George H. Dunteman and Moon-Ho R. Ho. An Introduction to Generalized Linear Models. SAGE Publications, Inc., Thousand Oaks, CA, USA, 2006.
10. Caroline Farmer, Elisabetta Fenu, Norma O'Flynn, and Bruce Guthrie. Clinical assessment and management of multimorbidity: summary of nice guidance. BMJ, 354, 2016.
11. Joseph Futoma, Mark Sendak, C. Blake Cameron, and Katherine Heller. Scalable joint modeling of longitudinal and point process data for disease trajectory prediction and improving management of chronic kidney disease. In Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI'16, pages 222-231, 2016.
12. Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in recurrent neural networks. In Advances in Neural Information Processing Systems, NIPS 2016, 2016.
13. Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, and Aram Galstyan. Multitask learning and benchmarking with clinical time series data. CoRR, abs/1703.07771, 2017. URL https://arxiv.org/abs/1703.07771.
14. Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, and Ruwanthi Kolamunnage-Dona. Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues. BMC Medical Research Methodology, 16(1):117, Sep 2016.
15. Joseph W Hogan and Nan M Laird. Increasing efficiency from censored survival data by using random effects to model longitudinal covariates. Statistical Methods in Medical Research, 7(1):28-48, 1998.
16. Joseph G. Ibrahim, Ming Hui Chen, and Debajyoti Sinha. Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials. Statistica Sinica, 14(3):863-883, 7 2004.
17. Joseph G. Ibrahim, Haitao Chu, and Liddy M. Chen. Basic concepts and methods for joint models of longitudinal and survival data. Journal of Clinical Oncology, 28(16):27962801, 2010.
18. Anders Boeck Jensen, Pope L. Moseley, Tudor I. Oprea1, Sabrina Gade Ellese, Robert Eriksson, Henriette Schmock, Peter Bjdstrup Jensen, Lars Juh, Jensen, and Sren Brunak. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nature Communications., 5(4022), 2014.
19. Venkateshan Kannan, Narsis A. Kiani, Fredrik Piehl, and Jesper Tegner. A minimal unified model of disease trajectories captures hallmarks of multiple sclerosis. Mathematical Biosciences, 289:1-8, 2017.
20. Jared Katzman, Uri Shaham, Jonathan Bates, Alexander Cloninger, Tingting Jiang, and Yuval Kluger. Deepsurv: Personalized treatment recommender system using a cox proportional hazards deep neural network. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, ICML 2016, 2016.
21. Rahul G. Krishnan, Uri Shalit, and David Sontag. Deep kalman filters. CoRR, abs/1511.05121, 2017. URL https://arxiv.org/abs/1511.05121.
22. C. Lee, W. R. Zame, J. Yoon, and M. van der Schaar. Deephit: A deep learning approach to survival analysis with competing risks. In AAAI, 2018.
23. Zachary C. Lipton, David C. Kale, Charles Elkan, and Randall Wetzel. Learning to diagnose with lstm recurrent neural networks. In International Conference on Learning Representations (ICLR), 2016.
24. Margaux Luck, Tristan Sylvain, Héloïse Cardinal, Andrea Lodi, and Yoshua Bengio. Deep learning for patient-specific kidney graft survival analysis. CoRR, abs/1705.10245, 2017. URL http://arxiv.org/abs/1705.10245.
25. Todd MacKenzie, Alex H. Gifford, Kathryn A. Sabadosa, Hebe B. Quinton, Emily A. Knapp, Christopher H. Goss, and Bruce C. Marshall. Longevity of patients with cystic fibrosis in 2000 to 2010 and beyond: Survival analysis of the cystic fibrosis foundation patient registry. I Annals of internal medicine, 161(4):233-241, 2014.
26. Rajesh Ranganath, Adler Perotte, Nomie Elhadad, and David Blei. Deep survival analysis. In Proceedings of the 1st Machine Learning for Healthcare Conference, volume 56 of Proceedings of Machine Learning Research, pages 101-114, 18-19 Aug 2016.
27. Narges Razavian, Jake Marcus, and David Sontag. Multi-task prediction of disease onsets from longitudinal laboratory tests. In Proceedings of the 1st Machine Learning for Healthcare Conference (MLHC), volume 56 of Proceedings of Machine Learning Research, pages 73-100, Children's Hospital LA, Los Angeles, CA, USA, 18-19 Aug 2016.
28. Dimitris Rizopoulos, Laura A. Hatfield, Bradley P. Carlin, and Johanna J. M. Takkenberg. Combining dynamic predictions from joint models for longitudinal and time-to-event data using bayesian model averaging. Journal of the American Statistical Association, 109 (508):1385-1397, 2014.
29. Dimitris Rizopoulos, Geert Molenberghs, and Emmanuel M.E.H. Lesaffre. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking. Biometrical Journal, 59(6):1521-4036, 2017.
30. Takaya Saito and Marc Rehmsmeier. The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PLoS One, 10(23), 2015.
31. Peter Schulam and Suchi Saria. Reliable decision support using counterfactual models. In Proceedings of the thirty-first Conference on Neural Information Processing Systems, (NIPS), 2017.
32. H. Soleimani, J. Hensman, and S. Saria. Scalable joint models for reliable uncertainty-aware event prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (8):1948-1963, 2018.
33. D. A. Stern, W. J. Morgan, A. L. Wright, S. Guerra, and F. D. Martinez. Poor airway function in early infancy and lung function by age 22 years: a non-selective longitudinal cohort study. Lancet, 370, 2007.
34. Terry M Therneau. A Package for Survival Analysis in S, 2015. URL https://CRAN.R-project.org/package=survival. version 2.38.
35. C Van Diemen, D Postma, M Siedlinski, A Blokstra, H Smit, and H. Boezen. Genetic variation in timp1 but not mmps predict excess fev1 decline in two general population-based cohorts. Respiratory Research., 12(1), 2011.
36. Marieke van Horck, Bjorn Winkens, Geertjan Wesseling, Dillys van Vliet, Kim van de Kant, Sanne Vaassen, Karin de Winter-de Groot, Ilja de Vreede, Quirijn Jbsis, and Edward Dompeling. Early detection of pulmonary exacerbations in children with cystic fibrosis by electronic home monitoring of symptoms and lung function. Scientific Reports, 7(1), 2017.
37. Hans van Houwelingen and Hein Putter. Dynamic Prediction in Clinical Survival Analysis. CRC Press, Inc., Boca Raton, FL, USA, 2011.
38. Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein, Thomas Kneib, Tania Pressler, Matthias Schmid, and Andreas Mayr. Boosting joint models for longitudinal and time-to-event data. Biometrical Journal, 59(6):1104-112, 2017.
39. Lang Wu. Mixed Effects Models for Complex Data. Chapman & Hall/CRC, Hoboken, NJ, USA, 2009.
40. Xiaolin Xu, Gita D. Mishra, and Mark Jones. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Ageing Research Reviews, 37:53-68, 2017.