SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers

• URL: http://arxiv.org/abs/2411.13428v1
• Date: Wed, 20 Nov 2024 16:11:20 GMT
• Title: SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers
• Authors: Hojjat Karami, David Atienza, Anisoara Ionescu,
• Abstract summary: We propose a novel tokenization strategy tailored for structured EHR data. We benchmark the fidelity, utility, and privacy of the generated data against state-of-the-art models.
• Score: 3.9018723423306003
• License:
• Abstract: 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.

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