A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercia
Paper PDF: http://arxiv.org/pdf/2504.14657v1
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Synthetic Electronic Health Records (EHRs) offer a valuable opportunity to
create privacy preserving and harmonized structured data, supporting numerous
applications in healthcare. Key benefits of synthetic data include precise
control over the data schema, improved fairness and representation of patient
populations, and the ability to share datasets without concerns about
compromising real individuals privacy. Consequently, the AI community has
increasingly turned to Large Language Models (LLMs) to generate synthetic data
across various domains. However, a significant challenge in healthcare is
ensuring that synthetic health records reliably generalize across different
hospitals, a long standing issue in the field. In this work, we evaluate the
current state of commercial LLMs for generating synthetic data and investigate
multiple aspects of the generation process to identify areas where these models
excel and where they fall short. Our main finding from this work is that while
LLMs can reliably generate synthetic health records for smaller subsets of
features, they struggle to preserve realistic distributions and correlations as
the dimensionality of the data increases, ultimately limiting their ability to
generalize across diverse hospital settings.
Видео A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercia канала AI Papers - Vuk Rosić
Check my merch: https://dragonprof-2.creator-spring.com
Synthetic Electronic Health Records (EHRs) offer a valuable opportunity to
create privacy preserving and harmonized structured data, supporting numerous
applications in healthcare. Key benefits of synthetic data include precise
control over the data schema, improved fairness and representation of patient
populations, and the ability to share datasets without concerns about
compromising real individuals privacy. Consequently, the AI community has
increasingly turned to Large Language Models (LLMs) to generate synthetic data
across various domains. However, a significant challenge in healthcare is
ensuring that synthetic health records reliably generalize across different
hospitals, a long standing issue in the field. In this work, we evaluate the
current state of commercial LLMs for generating synthetic data and investigate
multiple aspects of the generation process to identify areas where these models
excel and where they fall short. Our main finding from this work is that while
LLMs can reliably generate synthetic health records for smaller subsets of
features, they struggle to preserve realistic distributions and correlations as
the dimensionality of the data increases, ultimately limiting their ability to
generalize across diverse hospital settings.
Видео A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercia канала AI Papers - Vuk Rosić
AI-Driven Synthetic Electronic Health Records Challenges in Synthetic Data Generalization Commercial LLMs in Healthcare Data Synthesis Data Schema Control using Large Language Models Fairness in Synthetic Patient Population Representation LLM Evaluation for High-Dimensional Health Data Limitations of LLMs in Realistic Data Distributions Privacy-Preserving Synthetic Medical Records Synthetic EHR Generation with LLMs
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24 апреля 2025 г. 14:06:00
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