2 citations · 3 across the 4 of their papers we have counts for
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…
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…
Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression
Yao-Shun Chuang, Chun-Teh Lee, Ryan Brandon +4
This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance o…