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Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models.
Noninvasive positive pressure ventilation via face mask: first-line intervention in patients with acute hypercapnic and hypoxemic respiratory failure
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Random forests
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Frustratingly easy domain adaptation
Hal Daumé III · 2007
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Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert MÞller · 2007
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Survival analysis: time-dependent effects and time-varying risk factors
Friedo W Dekker, Renée De Mutsert, Paul C Van Dijk, Carmine Zoccali, and Kitty J Jager · 2008
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Variables with time-varying effects and the Cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Domain adaptation for large-scale sentiment classification: A deep learning approach
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Supervised sequence labelling
Alex Graves · 2012
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Epidemiology and outcomes of acute respiratory failure in the united states, 2001 to 2009: A national survey
Mihaela S Stefan, Meng-Shiou Shieh, Penelope S Pekow, Michael B Rothberg, Jay S Steingrub, Tara Lagu, and Peter K Lindenauer · 2013
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Learning factored representations in a deep mixture of experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Vasopressors for the treatment of septic shock: systematic review and meta-analysis
Tomer Avni, Adi Lador, Shaul Lev, Leonard Leibovici, Mical Paul, and Alon Grossman · 2015
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Adam: a method for stochastic optimization (2014)
Diederik Kingma and Jimmy Ba · 2015
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Doubly robust covariate shift correction
Sashank Jakkam Reddi, Barnabas Poczos, and Alex Smola · 2015
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Supervised representation learning: Transfer learning with deep autoencoders
Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, and Qing He · 2015
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Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2016
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Fast unsupervised online drift detection using incremental kolmogorov-smirnov test
Denis Moreira dos Reis, Peter Flach, Stan Matwin, and Gustavo Batista · 2016
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Definition, classification, etiology, and pathophysiology of shock in adults
David F Gaieski and ME Mikkelsen · 2016
Shortfuse: Biomedical time series representations in the presence of structured information
Madalina Fiterau, Suvrat Bhooshan, Jason Fries, Charles Bournhonesque, Jennifer Hicks, Eni Halilaj, Christopher Re, and Scott Delp · 2017
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Hypernetworks
David Ha, Andrew Dai, and Quoc V Le · 2017
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Early improving recurrent elastic highway network
Hyunsin Park and Chang D Yoo · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Domain adaptation with conditional transferable components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf · 2016
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Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 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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Grad-cam: Visual explanations from deep networks via gradient-based localization., in ‘iccv’, 2016
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra, et al · 2016
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Cluster adaptive training for deep neural network based acoustic model
Tian Tan, Yanmin Qian, and Kai Yu · 2016
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Deep reinforcement learning with double q-learning
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Aaron Fisher, Cynthia Rudin, and Francesca Dominici · 2018
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi · 2018
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Michaela Hardt, Peter J Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, et al · 2018
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Adapting to concept drift in credit card transaction data streams using contextual bandits and decision trees
Dennis JNJ Soemers, Tim Brys, Kurt Driessens, Mark HM Winands, and Ann Nowé · 2018
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Can deep clinical models handle real-world domain shifts?
Jayaraman J Thiagarajan, Deepta Rajan, and Prasanna Sattigeri · 2018
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Skipnet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E Gonzalez · 2018
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
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Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan · 2019
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Learning implicitly recurrent CNNs through parameter sharing
Pedro Savarese and Michael Maire · 2019
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