Learning and visualizing chronic latent representations using electronic health records.

ABSTRACT:

Background

Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches.

Methods

We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinica

SUBMITTER: Chushig-Muzo D 

PROVIDER: S-EPMC9446539 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature