Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation.

ABSTRACT:

Background

Electronic health records are a valuable source of patient information that must be properly deidentified before being shared with researchers. This process requires expertise and time. In addition, synthetic data have considerably reduced the restrictions on the use and sharing of real data, allowing researchers to access it more rapidly with far fewer privacy constraints. Therefore, there has been a growing interest in establishing a method to generate synthetic data that protects patients' privacy while properly reflecting the data.

Objective

This study aims to develop and validate a model that generates valuable synthetic longitudinal health data while protecting the privacy of the patients whose data are collected.

Methods

We investigated the best mode

SUBMITTER: Karimian Sichani E 

PROVIDER: S-EPMC11034549 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature