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Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time.
Increasing efficiency from censored survival data by using random effects to model longitudinal covariates
Joseph W Hogan and Nan M Laird · 1998
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Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials
Joseph G. Ibrahim, Ming Hui Chen, and Debajyoti Sinha · 2004
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An Introduction to Generalized Linear Models
George H. Dunteman and Moon-Ho R. Ho · 2006
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Poor airway function in early infancy and lung function by age 22 years: a non-selective longitudinal cohort study
D. A. Stern, W. J. Morgan, A. L. Wright, S. Guerra, and F. D. Martinez · 2007
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Mixed Effects Models for Complex Data
Lang Wu · 2009
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Basic concepts and methods for joint models of longitudinal and survival data
Joseph G. Ibrahim, Haitao Chu, and Liddy M. Chen · 2010
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Genetic variation in timp1 but not mmps predict excess fev1 decline in two general population-based cohorts
C Van Diemen, D Postma, M Siedlinski, A Blokstra, H Smit, and H. Boezen · 2011
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Dynamic Prediction in Clinical Survival Analysis
Hans van Houwelingen and Hein Putter · 2011
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Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients
Anders Boeck Jensen, Pope L. Moseley, Tudor I. Oprea1, Sabrina Gade Ellesøe, Robert Eriksson, Henriette Schmock, Peter Bjødstrup Jensen, Lars Juh, Jensen, and Søren Brunak · 2014
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Longevity of patients with cystic fibrosis in 2000 to 2010 and beyond: Survival analysis of the cystic fibrosis foundation patient registry
Todd MacKenzie, Alex H. Gifford, Kathryn A. Sabadosa, Hebe B. Quinton, Emily A. Knapp, Christopher H. Goss, and Bruce C. Marshall · 2014
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Combining dynamic predictions from joint models for longitudinal and time-to-event data using bayesian model averaging
Dimitris Rizopoulos, Laura A. Hatfield, Bradley P. Carlin, and Johanna J. M. Takkenberg · 2014
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Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference
Jessica Barrett, Peter Diggle, Robin Henderson, and David Taylor-Robinson · 2015
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Fitting linear mixed-effects models using lme4
Douglas Bates, Martin Mächler, Ben Bolker, and Steve Walker · 2015
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A robust deep model for improved classification of ad/mci patients
F Li, L Tran, K-H Thung, S Ji, D Shen, and J. Li · 2015
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The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets
Takaya Saito and Marc Rehmsmeier · 2015
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A Package for Survival Analysis in S , 2015
Terry M Therneau · 2015
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Evidence on multimorbidity from definition to intervention: An overview of systematic reviews
Xiaolin Xu, Gita D. Mishra, and Mark Jones · 2015
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Clinical assessment and management of multimorbidity: summary of nice guidance
Caroline Farmer, Elisabetta Fenu, Norma O’Flynn, and Bruce Guthrie · 2016
Multi-task prediction of disease onsets from longitudinal laboratory tests
Narges Razavian, Jake Marcus, and David Sontag · 2016
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Dpscreen: Dynamic personalized screening
Kartik Ahuja, William Zame, and Mihaela van der Schaar · 2017
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Deep multi-task gaussian processes for survival analysis with competing risks
A. M. Alaa and M. van der Schaar · 2017
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Dynamic predictions using flexible joint models of longitudinal and time‐to‐event data
J Barrett and L. Su · 2017
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Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, and Aram Galstyan · 2017
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Scalable joint modeling of longitudinal and point process data for disease trajectory prediction and improving management of chronic kidney disease
Joseph Futoma, Mark Sendak, C. Blake Cameron, and Katherine Heller · 2016
Cited alongside, same era.
A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues
Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, and Ruwanthi Kolamunnage-Dona · 2016
Cited alongside, same era.
Deepsurv: Personalized treatment recommender system using a cox proportional hazards deep neural network
Jared Katzman, Uri Shaham, Jonathan Bates, Alexander Cloninger, Tingting Jiang, and Yuval Kluger · 2016
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Learning to diagnose with lstm recurrent neural networks
Zachary C. Lipton, David C. Kale, Charles Elkan, and Randall Wetzel · 2016
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Deep survival analysis
Rajesh Ranganath, Adler Perotte, Noémie Elhadad, and David Blei · 2016
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Venkateshan Kannan, Narsis A. Kiani, Fredrik Piehl, and Jesper Tegner · 2017
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Rahul G. Krishnan, Uri Shalit, and David Sontag · 2017
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Deep learning for patient-specific kidney graft survival analysis
Margaux Luck, Tristan Sylvain, Héloïse Cardinal, Andrea Lodi, and Yoshua Bengio · 2017
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Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking
Dimitris Rizopoulos, Geert Molenberghs, and Emmanuel M.E.H. Lesaffre · 2017
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Early detection of pulmonary exacerbations in children with cystic fibrosis by electronic home monitoring of symptoms and lung function
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 Jöbsis, and Edward Dompeling · 2017
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Boosting joint models for longitudinal and time-to-event data
Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein, Thomas Kneib, Tania Pressler, Matthias Schmid, and Andreas Mayr · 2017
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Deephit: A deep learning approach to survival analysis with competing risks
C. Lee, W. R. Zame, J. Yoon, and M. van der Schaar · 2018
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