The application of unsupervised deep learning in predictive models using electronic health records.
Journal:
BMC medical research methodology
Published Date:
Feb 26, 2020
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 on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks.