Search Results for author:
Found 16 papers, 6 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).
RiskAgent: Autonomous Medical AI Copilot for Generalist Risk Prediction
To improve the adaptability of our model in different scenarios, we have built and open-sourced a family of models ranging from 1 billion to 70 billion parameters.
SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation
Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians.
Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction
This study proposes an approach for error correction in radiology reports, leveraging large language models (LLMs) and retrieval-augmented generation (RAG) techniques.
Infusing clinical knowledge into tokenisers for language models
Specifically, using K-Tokeniser, the language models would only require 50\% of the training data to achieve the best performance of the baseline tokeniser using all training data in the concept extraction task and less than 20\% of the data for the automated coding task.
Chain-of-Though (CoT) prompting strategies for medical error detection and correction
We report results for three methods of few-shot In-Context Learning (ICL) augmented with Chain-of-Thought (CoT) and reason prompts using a large language model (LLM).
MedExQA: Medical Question Answering Benchmark with Multiple Explanations
This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models' (LLMs) understanding of medical knowledge through explanations.
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization
The approach involves two main steps: 1) re-training the BART model on a large corpus of radiology reports using a novel entity masking strategy to improving biomedical domain knowledge learning, and 2) fine-tuning the model for the summarization task using the Findings and Background sections to predict the Impression section.
A Hybrid Framework with Large Language Models for Rare Disease Phenotyping
Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations.
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.
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.
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.
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.
