35 citations · 45 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records
Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee +4
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT…