4 papers
Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction
Yao-Shun Chuang, Tushti Mody, Uday Pratap Singh +6
Clinical named entity recognition from dental progress notes is challenging because documentation is highly unstructured, domain-specific, and often privacy-sensitive. We developed…
Cross-Institutional Dental EHR Entity Extraction via Generative AI and Synthetic Notes
Yao-Shun Chuang, Chun-Teh Lee, Oluwabunmi Tokede +5
This research addresses the issue of missing structured data in dental records by extracting diagnostic information from unstructured text. The updated periodontology classificatio…
De-identification is not enough: a comparison between de-identified and synthetic clinical notes
Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed +1
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 alt…
Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks
Yao-Shun Chuang, Atiquer Rahman Sarkar, Yu-Chun Hsu +2
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…