Generating synthetic Electronic Health Records (EHRs) offers significant potential for data augmentation, privacy-preserving data sharing, and improving machine learning model training. We propose a novel tokenization strategy tailored for structured EHR data, which encompasses diverse data types such as covariates, ICD codes, and irregularly sampled time series. Using a GPT-like decoder-only transformer model, we demonstrate the generation of high-quality synthetic EHRs. Our approach is evaluated using the MIMIC-III dataset, and we benchmark the fidelity, utility, and privacy of the generated data against state-of-the-art models.
@article{arxiv.2411.13428,
title = {SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers},
author = {Hojjat Karami and David Atienza and Anisoara Ionescu},
journal= {arXiv preprint arXiv:2411.13428},
year = {2024}
}