Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Sarkar, Atiquer Rahman"'
This study examines integrating EHRs and NLP with large language models (LLMs) to improve healthcare data management and patient care. It focuses on using advanced models to create secure, HIPAA-compliant synthetic patient notes for biomedical resear
Externí odkaz:
http://arxiv.org/abs/2407.16166
Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better trade-off
Externí odkaz:
http://arxiv.org/abs/2407.07926
For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative
Externí odkaz:
http://arxiv.org/abs/2402.00179
Autor:
Sarkar, Atiquer Rahman1 (AUTHOR) sarkarar@myumanitoba.ca, Chuang, Yao-Shun2 (AUTHOR), Mohammed, Noman1 (AUTHOR), Jiang, Xiaoqian2 (AUTHOR)
Publikováno v:
Scientific Reports. 11/29/2024, Vol. 14 Issue 1, p1-12. 12p.
