Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study.

ABSTRACT:

Background

Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in health care include data augmentation by variational autoencoders (VAE) technology.

Objective

We aimed to test the feasibility of generating artificial patients with reliable clinical characteristics by using a geometry-based VAE applied, for the first time, on high-dimension, low-sample-size tabular data.

Methods

Clinical tabular data were extracted from 521 real patients of the "MAX" digital conversational agent (BOTdesign) created for preparing patients for anesthesia. A 3-stage methodological approach was implemented to generate up to 10,000 artificial patients: training th

SUBMITTER: Ferre F 

PROVIDER: S-EPMC12046256 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature