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Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations.
Learning internal representations by error propagation
Rumelhart, David E, Hinton, Geoffrey E, and Williams, Ronald J · 1985
Earlier work this paper cites.
Finding structure in time
Elman, Jeffrey L · 1990
Earlier work this paper cites.
Application of artificial neural networks to clinical medicine
Baxt, W.G · 1995
Earlier work this paper cites.
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Caruana, Rich, Baluja, Shumeet, Mitchell, Tom, et al · 1996
Earlier work this paper cites.
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Pollack, M. M., Patel, K. M., and Ruttimann, U. E · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
Earlier work this paper cites.
Adaptive blind signal processing-neural network approaches
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents
National High Blood Pressure Education Program Working Group on Children and Adolescents · 2004
Earlier work this paper cites.
International statistical classification of diseases and related health problems , volume 1
World Health Organization · 2004
Earlier work this paper cites.
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Earlier work this paper cites.
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Xu, Rui, Wunsch II, Donald, and Frank, Ronald · 2007
Earlier work this paper cites.
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Aleks, Norm, Russell, Stuart J, Madden, Michael G, Morabito, Diane, Staudenmayer, Kristan, Cohen, Mitchell, and Manley, Geoffrey T · 2009
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Quinn, John, Williams, Christopher KI, McIntosh, Neil, et al · 2009
Cited alongside, same era.
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Murray and Nadel’s textbook of respiratory medicine: 2-volume set
Mason, Robert J., Broaddus, V. Courtney, Martin, Thomas, King Jr., Talmadge E., Schraufnagel, Dean, Murray, John F., and Nadel, Jay A · 2010
Cited alongside, same era.
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Sutskever, Ilya, Vinyals, Oriol, and Le, Quoc VV · 2014
Later among the works it cites.
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Zaremba, Wojciech, Sutskever, Ilya, and Vinyals, Oriol · 2014
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De Mulder, Wim, Bethard, Steven, and Moens, Marie-Francine · 2015
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