The impact of extraneous features on the performance of recurrent neural network models in clinical tasks-Reference-Cited by-同舟云学术

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The impact of extraneous features on the performance of recurrent neural network models in clinical tasks

Published:2020-02 Issue: Volume:102 Page:103351
ISSN:1532-0464
Container-title:Journal of Biomedical Informatics
language:en
Short-container-title:Journal of Biomedical Informatics

Author:

Laksana Eugene,Aczon Melissa,Ho Long,Carlin Cameron^ORCID,Ledbetter David,Wetzel Randall

Funder

L. K. Whittier Foundation

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference19 articles.

1. Adoption of electronic health record systems among US non-federal acute care hospitals: 2008–2015;Henry;ONC Data Brief.,2016

2. Deep learning;LeCun;Nature,2015

3. The elements of statistical learning: data mining, inference, and prediction;Trevor,2009

4. Dropout: a simple way to prevent neural networks from overfitting;Srivastava;J. Mach. Learn. Res.,2014

5. A theoretically grounded application of dropout in recurrent neural networks;Gal;Adv. Neural Inf. Process. Syst.,2016

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