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There is a great need for technologies that can predict the mortality of patients in intensive care units with both high accuracy and accountability.
Optimizing Area Under Roc Curve with SVMs
Rakotomamonjy, Alain · 2004
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Topic modeling: beyond bag-of-words
Wallach, H · 2006
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Replicated Softmax: an Undirected Topic Model
Hinton, Geoffrey E and Salakhutdinov, Ruslan R · 2009
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Computationally Generated Cardiac Biomarkers: Heart Rate Patterns to Predict Death Following Coronary Attacks
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Modeling Documents with Deep Boltzmann Machines
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Online Latent Dirichlet Allocation with Infinite Vocabulary
Zhai, Ke and Boyd-Graber, Jordan L · 2013
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Scalable noise mining in long-term electrocardiographic time-series to predict death following heart attacks
Chia, C C and Syed, Z · 2014
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Unfolding Physiological State: Mortality Modelling in Intensive Care Units
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A novel neural topic model and its supervised extension
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Jo, Yohan and Rosé, Carolyn Penstein · 2015
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Learning to Diagnose with LSTM Recurrent Neural Networks
Hierarchical multiscale recurrent neural networks
Chung, Junyoung, Ahn, Sungjin, and Bengio, Yoshua · 2016
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Topicrnn: A recurrent neural network with long-range semantic dependency
Dieng, Adji B., Wang, Chong, Gao, Jianfeng, and Paisley, John William · 2016
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Neural Document Embeddings for Intensive Care Patient Mortality Prediction
Grnarova, Paulina, Schmidt, Florian, Hyland, Stephanie L, and Eickhoff, Carsten · 2016
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MIMIC-III, a freely accessible critical care database
Johnson, Alistair E W, Pollard, Tom J, Shen, Lu, Lehman, Li-wei H, Feng, Mengling, Ghassemi, Mohammad, Moody, Benjamin, Szolovits, Peter, Anthony Celi, Leo, and Mark, Roger G · 2016
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Learning from LDA using deep neural networks
Zhang, Dongxu, Luo, Tianyi, and Wang, Dong · 2016
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Lipton, Zachary C, Kale, David C, Elkan, Charles, and Wetzell, Randall · 2015
Cited alongside, same era.