no code implementations • 19 Apr 2024 • Chia-Hsuan Chang, Mary M. Lucas, Yeawon Lee, Christopher C. Yang, Grace Lu-Yao
Using an open access clinical large language model to determine the pathologic cancer stage from real-world pathology reports, we show that the ensemble reasoning approach is able to improve both the consistency and performance of the LLM in determining cancer stage, thereby demonstrating the potential to use these models in clinical or other domains where reliability and trustworthiness are critical.
no code implementations • 19 Apr 2024 • Chia-Hsuan Chang, Xiaoyang Wang, Christopher C. Yang
By focusing on the predictive modeling of sepsis-related mortality, we propose a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness.
no code implementations • 4 Apr 2024 • Mary M. Lucas, Xiaoyang Wang, Chia-Hsuan Chang, Christopher C. Yang, Jacqueline E. Braughton, Quyen M. Ngo
Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government.
no code implementations • 2 Apr 2024 • Chia-Hsuan Chang, Mary M. Lucas, Grace Lu-Yao, Christopher C. Yang
Cancer stage classification is important for making treatment and care management plans for oncology patients.
no code implementations • 23 Jun 2022 • Chia-Hsuan Chang, Lei Wang, Christopher C. Yang
The experimental results demonstrate that our framework outperforms the other two large language models in identifying CHV across languages.
1 code implementation • 12 May 2021 • Ali Jazayeri, Christopher C. Yang
In nearly all the algorithms proposed for frequent pattern mining in temporal networks, the networks are represented as sequences of static networks.
no code implementations • WS 2018 • Mengnan Zhao, Aaron J. Masino, Christopher C. Yang
We investigate the quality of task specific word embeddings created with relatively small, targeted corpora.