Accelerating Reproducible Research in Synthetic EHR Generation
Researchers have developed a new framework to standardize the evaluation of synthetic Electronic Health Record (EHR) generation models. This framework addresses the challenges of comparing different models due to inconsistent codebases and evaluation methods. It includes a unified pipeline for data ingestion, model training, and architecture-agnostic evaluation, focusing initially on longitudinal ICD diagnosis codes and incorporating baselines like MedGAN, CorGAN, PromptEHR, HALO, and GPT-2. AI
IMPACT Standardizes evaluation for synthetic EHR models, potentially accelerating research and improving privacy-utility trade-offs.