no code implementations • 31 Jan 2024 • Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang
In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated the performance of the synthetically generated notes in a clinical domain task, and (iv) proposed a way to mount a membership inference attack where the target model is trained with synthetic data.
no code implementations • 17 Nov 2023 • Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee, Ryan Brandon, Duong Tran, Oluwabunmi Tokede, Muhammad F. Walji
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt.
no code implementations • 17 Nov 2023 • Yao-Shun Chuang, Chun-Teh Lee, Ryan Brandon, Trung Duong Tran, Oluwabunmi Tokede, Muhammad F. Walji, Xiaoqian Jiang
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 of a named entity recognition (NER) model on different regular expression (RE) methods.