Search Results for author: Abul Hasan

Found 6 papers, 0 papers with code

Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction

no code implementations21 Jun 2024 Jinge Wu, Zhaolong Wu, Ruizhe Li, Abul Hasan, Yunsoo Kim, Jason P. Y. Cheung, Teng Zhang, Honghan Wu

This study proposes an approach for error correction in radiology reports, leveraging large language models (LLMs) and retrieval-augmented generation (RAG) techniques.

RAG Retrieval

Infusing clinical knowledge into tokenisers for language models

no code implementations20 Jun 2024 Abul Hasan, Jinge Wu, Quang Ngoc Nguyen, Salomé Andres, Imane Guellil, Huayu Zhang, Arlene Casey, Beatrice Alex, Bruce Guthrie, Honghan Wu

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.

Clinical Knowledge Relation Extraction

Chain-of-Though (CoT) prompting strategies for medical error detection and correction

no code implementations13 Jun 2024 Zhaolong Wu, Abul Hasan, Jinge Wu, Yunsoo Kim, Jason P. Y. Cheung, Teng Zhang, Honghan Wu

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).

In-Context Learning Language Modeling +2

RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization

no code implementations5 Jun 2024 Jinge Wu, Abul Hasan, Honghan Wu

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.

Clinical Knowledge Denoising +2

Incorporating Dictionaries into a Neural Network Architecture to Extract COVID-19 Medical Concepts From Social Media

no code implementations5 Sep 2023 Abul Hasan, Mark Levene, David Weston

A major difficulty in medical concept extraction is obtaining labelled data from which to build supervised models.

Monitoring Covid-19 on social media using a novel triage and diagnosis approach

no code implementations22 Mar 2021 Abul Hasan, Mark Levene, David Weston, Renate Fromson, Nicolas Koslover, Tamara Levene

An unsupervised rule-based algorithm is then applied to establish relations between concepts in the next step of the pipeline.

BIG-bench Machine Learning

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