Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study.

ABSTRACT:

Background

By analyzing electronic health record snapshots of similar patients, physicians can proactively predict disease onsets, customize treatment plans, and anticipate patient-specific trajectories. However, the modeling of electronic health record data is inherently challenging due to its high dimensionality, mixed feature types, noise, bias, and sparsity. Patient representation learning using autoencoders (AEs) presents promising opportunities to address these challenges. A critical question remains: how do different AE designs and distance measures impact the quality of retrieved similar patient cohorts?

Objective

This study aims to evaluate the performance of 5 common AE variants-vanilla autoencoder, denoising autoencoder, contractive autoencoder, sparse autoencoder

SUBMITTER: Li D 

PROVIDER: S-EPMC12289314 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature