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 on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks.

Methods

We compare the model with autoencoder features to traditional models: logistic model with least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. In addition, we include a predictive model using a small subset of response-specific variables (Simple Reg) and a model combining these variables with features from autoencoder (Enhanced Reg). We perfor

SUBMITTER: Wang L 

PROVIDER: S-EPMC7043035 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature