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CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines

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arxiv 2402.04400 v2 pith:UINSPMBQ submitted 2024-02-06 cs.LG cs.AIcs.CY

CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines

Chao Pang , Xinzhuo Jiang , Nishanth Parameshwar Pavinkurve , Krishna S. Kalluri , Elise L. Minto , Jason Patterson , Linying Zhang , George Hripcsak
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Gamze G\"ursoy No\'emie Elhadad Karthik Natarajan
This is my paper
classification cs.LG cs.AIcs.CY
keywords datapatientsyntheticapplicationselectronicformatgenerategeneration
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data. Although existing methods, like rule-based approaches and generative adversarial networks (GANs), generate synthetic data that resembles real-world EHR data, these methods often use a tabular format, disregarding temporal dependencies in patient histories and limiting data replication. Recently, there has been a growing interest in leveraging Generative Pre-trained Transformers (GPT) for EHR data. This enables applications like disease progression analysis, population estimation, counterfactual reasoning, and synthetic data generation. In this work, we focus on synthetic data generation and demonstrate the capability of training a GPT model using a particular patient representation derived from CEHR-BERT, enabling us to generate patient sequences that can be seamlessly converted to the Observational Medical Outcomes Partnership (OMOP) data format.

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