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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
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
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.
Hallucination Benchmark in Medical Visual Question Answering
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.
Exploring Multimodal Large Language Models for Radiology Report Error-checking
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
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
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
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
This paper intends to apply the Hidden Markov Model into stock market and and make predictions.
