no code implementations • 27 Feb 2024 • Junda Wang, Zhichao Yang, Zonghai Yao, Hong Yu
To further improve the performance of these systems in the medical domain, we introduce an innovative method that jointly trains an Information Retrieval (IR) system and an LLM during the fine-tuning phase.
1 code implementation • 24 Oct 2023 • Junda Wang, Zonghai Yao, Zhichao Yang, Huixue Zhou, Rumeng Li, Xun Wang, Yucheng Xu, Hong Yu
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues.
no code implementations • 23 Oct 2023 • Kaixin Liu, Jiwei Zhou, Junda Wang
Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%.
1 code implementation • 29 Jun 2023 • Junda Wang, Zonghai Yao, Avijit Mitra, Samuel Osebe, Zhichao Yang, Hong Yu
This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets.
no code implementations • 29 Sep 2022 • Junda Wang, Weijian Li, Han Wang, Hanjia Lyu, Caroline Thirukumaran, Addisu Mesfin, Jiebo Luo
Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics.
no code implementations • 18 Jun 2021 • Junda Wang, Xupin Zhang, Jiebo Luo
More importantly, sentiment analysis and the paired sample t-test are performed to examine the differences in crowdfunding campaigns before and after the COVID-19 outbreak that started in March 2020.
no code implementations • 3 Dec 2020 • Hanjia Lyu, Wei Wu, Junda Wang, Viet Duong, Xiyang Zhang, Jiebo Luo
People who have the worst personal pandemic experience are more likely to hold the anti-vaccine opinion.
Social and Information Networks