Poster
in
Workshop: GenAI for Health: Potential, Trust and Policy Compliance
SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers
Hojjat Karami · David Atienza · Anisoara Paraschiv-Ionescu
Keywords: [ irregularly-sampled time series ] [ Transformers ] [ EHR ] [ synthetic data generation ]
Generating synthetic Electronic Health Records (EHRs) offers significant potential for data augmentation, privacy-preserving data sharing, and enhancing 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. Utilizing 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.