The application of unsupervised deep learning in predictive models using electronic health records

The application of unsupervised deep learning in predictive models using electronic health records

Abstract Background The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses...

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Bibliographic Details
Main Authors: Lei Wang, Liping Tong, Darcy Davis, Tim Arnold, Tina Esposito
Format: Article
Language:English
Published: BMC 2020-02-01
Series:BMC Medical Research Methodology
Subjects:
Autoencoder
LASSO
Enhanced Reg
Predictive model
Predictive performance
Important response-specific predictors
Online Access:http://link.springer.com/article/10.1186/s12874-020-00923-1

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http://link.springer.com/article/10.1186/s12874-020-00923-1

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