Fetching the paper…
Reading the bibliography…
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.
Some different types of essential hypertension: their course and prognosis
Norman M Keith, Henry P Wagener, and Nelson W Barker · 1939
Earlier work this paper cites.
Diabetic cataract formation: potential role of glycosylation of lens crystallins
Victor J Stevens, Carol A Rouzer, Vincent M Monnier, and Anthony Cerami · 1978
Earlier work this paper cites.
Essential hypertension and cognitive function. the role of hyperinsulinemia
Johanna Kuusisto, Keijo Koivisto, L Mykkänen, Eeva-Liisa Helkala, Matti Vanhanen, T Hänninen, K Pyörälä, Paavo Riekkinen, and Markku Laakso · 1993
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Continuous time bayesian networks
Uri Nodelman, Christian R Shelton, and Daphne Koller · 2002
Earlier work this paper cites.
Multistate markov models for disease progression with classification error
Christopher H Jackson, Linda D Sharples, Simon G Thompson, Stephen W Duffy, and Elisabeth Couto · 2003
Earlier work this paper cites.
A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects
Wilson Truccolo, Uri T Eden, Matthew R Fellows, John P Donoghue, and Emery N Brown · 2005
Earlier work this paper cites.
A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying type 2 diabetes mellitus
Willem De Winter, Joost DeJongh, Teun Post, Bart Ploeger, Richard Urquhart, Ian Moules, David Eckland, and Meindert Danhof · 2006
Earlier work this paper cites.
A semi-markov model for multistate and interval-censored data with multiple terminal events. application in renal transplantation
Yohann Foucher, Magali Giral, Jean-Paul Soulillou, and Jean-Pierre Daures · 2007
Earlier work this paper cites.
Estimation of space–time branching process models in seismology using an em–type algorithm
Alejandro Veen and Frederic P Schoenberg · 2008
Earlier work this paper cites.
A novel connectionist system for unconstrained handwriting recognition
Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, and Jürgen Schmidhuber · 2009
Earlier work this paper cites.
Multivariate hawkes processes
Thomas Josef Liniger · 2009
Earlier work this paper cites.
Disease progression meta-analysis model in alzheimer’s disease
Kaori Ito, Sima Ahadieh, Brian Corrigan, Jonathan French, Terence Fullerton, Thomas Tensfeldt, Alzheimer’s Disease Working Group, et al · 2010
Earlier work this paper cites.
A predictive model for progression of chronic kidney disease to kidney failure
Navdeep Tangri, Lesley A Stevens, John Griffith, Hocine Tighiouart, Ognjenka Djurdjev, David Naimark, Adeera Levin, and Andrew S Levey · 2011
Earlier work this paper cites.
Theano: new features and speed improvements
Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, and Yoshua Bengio · 2012
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
Earlier work this paper cites.
Unsupervised and transfer learning challenge: a deep learning approach
Grégoire Mesnil, Yann Dauphin, Xavier Glorot, Salah Rifai, Yoshua Bengio, Ian J Goodfellow, Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, et al · 2012
Earlier work this paper cites.
Models for disease progression: new approaches and uses
DR Mould · 2012
Cited alongside, same era.
Disease progression modeling using hidden markov models
Rafid Sukkar, Edward Katz, Yanwei Zhang, David Raunig, and Bradley T Wyman · 2012
Cited alongside, same era.
Multiplicative forests for continuous-time processes
Jeremy Weiss, Sriraam Natarajan, and David Page · 2012
Cited alongside, same era.
Modeling disease progression via fused sparse group lasso
Jiayu Zhou, Jun Liu, Vaibhav A Narayan, and Jieping Ye · 2012
Cited alongside, same era.
Fast structure learning in generalized stochastic processes with latent factors
Mohammad Taha Bahadori, Yan Liu, and Eric P Xing · 2013
Cited alongside, same era.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pierre Vincent · 2013
Cited alongside, same era.
Lsda: Large scale detection through adaptation
Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, and Kate Saenko · 2014
Later among the works it cites.
Unifying visual-semantic embeddings with multimodal neural language models
Ryan Kiros, Ruslan Salakhutdinov, and Richard S Zemel · 2014
Later among the works it cites.
Latent Continuous Time Markov Chains for Partially-Observed Multistate Disease Processes
Jane Lange · 2014
Later among the works it cites.
Discovering latent network structure in point process data
Scott Linderman and Ryan Adams · 2014
Later among the works it cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le · 2014
Later among the works it cites.
Unsupervised learning of disease progression models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generating sequences with recurrent neural networks
Alex Graves · 2013
Cited alongside, same era.
Bayesian nonparametric hidden semi-markov models
Matthew J Johnson and Alan S Willsky · 2013
Cited alongside, same era.
Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data
Thomas A Lasko, Joshua C Denny, and Mia A Levy · 2013
Cited alongside, same era.
Longitudinal modeling of glaucoma progression using 2-dimensional continuous-time hidden markov model
Yu-Ying Liu, Hiroshi Ishikawa, Mei Chen, Gadi Wollstein, Joel S Schuman, and James M Rehg · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Cited alongside, same era.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
Cited alongside, same era.
Xiang Wang, David Sontag, and Fei Wang · 2014
Later among the works it cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Later among the works it cites.
Wojciech Zaremba and Ilya Sutskever · 2014
Later among the works it cites.
Deep computational phenotyping
Zhengping Che, David Kale, Wenzhe Li, Mohammad Taha Bahadori, and Yan Liu · 2015
Closest in time.
Constructing disease network and temporal progression model via context-sensitive hawkes process
Edward Choi, Nan Du, Robert Chen, Le Song, and Jimeng Sun · 2015
Closest in time.
Pd disease state assessment in naturalistic environments using deep learning
Nils Yannick Hammerla, James Fisher, Peter Andras, Lynn Rochester, Richard Walker, and Thomas Plötz · 2015
Closest in time.
A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data
Jane M Lange, Rebecca A Hubbard, Lurdes YT Inoue, and Vladimir N Minin · 2015
Closest in time.
The survival filter: Joint survival analysis with a latent time series
Rajesh Ranganath, Adler Perotte, Noémie Elhadad, and David M Blei · 2015
Closest in time.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Closest in time.
Learning to diagnose with lstm recurrent neural networks, 2016
Zachary C Lipton, David C Kale, Charles Elkan, and Randall Wetzell · 2016
Closest in time.
Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley · 2016
Closest in time.