no code implementations • SMM4H (COLING) 2022 • Imane Guellil, Jinge Wu, Honghan Wu, Tony Sun, Beatrice Alex
Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine).
no code implementations • 16 May 2024 • Jinge Wu, Hang Dong, Zexi Li, Arijit Patra, Honghan Wu
The infrequency and heterogeneity of clinical presentations in rare diseases often lead to underdiagnosis and their exclusion from structured datasets.
no code implementations • 3 Apr 2024 • Yunsoo Kim, Jinge Wu, Yusuf Abdulle, Yue Gao, Honghan Wu
This work proposes a novel approach to enhance human-computer interaction in chest X-ray analysis using Vision-Language Models (VLMs) enhanced with radiologists' attention by incorporating eye gaze data alongside textual prompts.
1 code implementation • 11 Jan 2024 • Jinge Wu, Yunsoo Kim, Honghan Wu
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potential of realizing effective visual assistants for healthcare.
no code implementations • 20 Dec 2023 • Jinge Wu, Yunsoo Kim, Eva C. Keller, Jamie Chow, Adam P. Levine, Nikolas Pontikos, Zina Ibrahim, Paul Taylor, Michelle C. Williams, Honghan Wu
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports.
1 code implementation • 9 Nov 2023 • Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S. Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Chenyu You, Xian Wu, Yefeng Zheng, Lei Clifton, Zheng Li, Jiebo Luo, David A. Clifton
Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.
no code implementations • 24 Aug 2022 • Jinge Wu, Rowena Smith, Honghan Wu
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence.
no code implementations • 24 Aug 2022 • Jinge Wu, Rowena Smith, Honghan Wu
In this paper, we present an ontology-driven self-supervised approach (derive concept embeddings using an auto-encoder from baseline NLP results) for producing a publicly available resource that would support large-scale machine learning (e. g., training transformer based large language models) on social media corpus.
1 code implementation • 20 Apr 2021 • Mingwen Liu, Junbang Huo, Yulin Wu, Jinge Wu
This paper intends to apply the Hidden Markov Model into stock market and and make predictions.