Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.

ABSTRACT:

Background

Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications).

Objective

This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient's mortality using their longitudinal EHR data.

Methods

We built a set of neural network models that we collectively called as lon

SUBMITTER: Rongali S 

PROVIDER: S-EPMC7136840 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature