CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913342218567680 |
|---|---|
| author | Pang, Chao Jiang, Xinzhuo Pavinkurve, Nishanth Parameshwar Kalluri, Krishna S. Minto, Elise L. Patterson, Jason Zhang, Linying Hripcsak, George Gürsoy, Gamze Elhadad, Noémie Natarajan, Karthik |
| author_facet | Pang, Chao Jiang, Xinzhuo Pavinkurve, Nishanth Parameshwar Kalluri, Krishna S. Minto, Elise L. Patterson, Jason Zhang, Linying Hripcsak, George Gürsoy, Gamze Elhadad, Noémie Natarajan, Karthik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines Pang, Chao Jiang, Xinzhuo Pavinkurve, Nishanth Parameshwar Kalluri, Krishna S. Minto, Elise L. Patterson, Jason Zhang, Linying Hripcsak, George Gürsoy, Gamze Elhadad, Noémie Natarajan, Karthik Machine Learning Artificial Intelligence Computers and Society 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. |
| title | CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2402.04400 |