Scalable and accurate deep learning with electronic health records

Artificial intelligence: Algorithm predicts clinical outcomes for hospital inpatients Artificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren f...

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Auteurs principaux:	Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg S. Corrado, Jeffrey Dean
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2018
Sujets:	Computer applications to medicine. Medical informatics R858-859.7
Accès en ligne:	https://doaj.org/article/a9b3a7cdda6e486cba268a16dc6760b2
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Scalable and accurate deep learning with electronic health records

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