Search Results for author: Jinge Wu

Found 8 papers, 3 papers with code

Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination

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).


Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns

no code implementations3 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.

Language Modelling Question Answering +1

Hallucination Benchmark in Medical Visual Question Answering

1 code implementation11 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.

Hallucination Medical Visual Question Answering +2

Exploring Multimodal Large Language Models for Radiology Report Error-checking

no code implementations20 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.

A Survey of Large Language Models in Medicine: Progress, Application, and Challenge

1 code implementation9 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.

Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLP

no code implementations24 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.

Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets

no code implementations24 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.

Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory

1 code implementation20 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.

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