%0 Generic
%T SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers
%I arXiv
%U http://arxiv.org/abs/2411.13428
%X 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.
%A Karami, Hojjat
%A Atienza, David
%A Ionescu, Anisoara
%D 2024-11-20
%K Computer Science - Artificial Intelligence
Computer Science - Machine Learning
preprint