Bevan Koopman


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Catching Misdiagnosed Limb Fractures in the Emergency Department Using Cross-institution Transfer Learning
Filip Rusak | Bevan Koopman | Nathan J. Brown | Kevin Chu | Jinghui Liu | Anthony Nguyen
Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association

We investigated the development of a Machine Learning (ML)-based classifier to identify abnormalities in radiology reports from Emergency Departments (EDs) that can help automate the radiology report reconciliation process. Often, radiology reports become available to the ED only after the patient has been treated and discharged, following ED clinician interpretation of the X-ray. However, occasionally ED clinicians misdiagnose or fail to detect subtle abnormalities on X-rays, so they conduct a manual radiology report reconciliation process as a safety net. Previous studies addressed this problem of automated reconciliation using ML-based classification solutions that require data samples from the target institution that is heavily based on feature engineering, implying lower transferability between hospitals. In this paper, we investigated the benefits of using pre-trained BERT models for abnormality classification in a cross-institutional setting where data for fine-tuning was unavailable from the target institution. We also examined how the inclusion of synthetically generated radiology reports from ChatGPT affected the performance of the BERT models. Our findings suggest that BERT-like models outperform previously proposed ML-based methods in cross-institutional scenarios, and that adding ChatGPT-generated labelled radiology reports can improve the classifier’s performance by reducing the number of misdiagnosed discharged patients.

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Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
Shengyao Zhuang | Bing Liu | Bevan Koopman | Guido Zuccon
Findings of the Association for Computational Linguistics: EMNLP 2023

In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document. Recently, advanced large language models (LLMs) have emerged as effective QLMs, showcasing promising ranking capabilities. This paper focuses on investigating the genuine zero-shot ranking effectiveness of recent LLMs, which are solely pre-trained on unstructured text data without supervised instruction fine-tuning. Our findings reveal the robust zero-shot ranking ability of such LLMs, highlighting that additional instruction fine-tuning may hinder effectiveness unless a question generation task is present in the fine-tuning dataset. Furthermore, we introduce a novel state-of-the-art ranking system that integrates LLM-based QLMs with a hybrid zero-shot retriever, demonstrating exceptional effectiveness in both zero-shot and few-shot scenarios. We make our codebase publicly available at

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Dr ChatGPT tell me what I want to hear: How different prompts impact health answer correctness
Bevan Koopman | Guido Zuccon
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

This paper investigates the significant impact different prompts have on the behaviour of ChatGPT when used for health information seeking. As people more and more depend on generative large language models (LLMs) like ChatGPT, it is critical to understand model behaviour under different conditions, especially for domains where incorrect answers can have serious consequences such as health. Using the TREC Misinformation dataset, we empirically evaluate ChatGPT to show not just its effectiveness but reveal that knowledge passed in the prompt can bias the model to the detriment of answer correctness. We show this occurs both for retrieve-then-generate pipelines and based on how a user phrases their question as well as the question type. This work has important implications for the development of more robust and transparent question-answering systems based on generative large language models. Prompts, raw result files and manual analysis are made publicly available at

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e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation
Aaron Nicolson | Jason Dowling | Bevan Koopman
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks

We describe the participation of team e-Health CSIRO in the BioNLP RadSum task of 2023. This task aims to develop automatic summarisation methods for radiology. The subtask that we participated in was multimodal; the impression section of a report was to be summarised from a given findings section and set of Chest X-rays (CXRs) of a subject’s study. For our method, we adapted an encoder-to-decoder model for CXR report generation to the subtask. e-Health CSIRO placed seventh amongst the participating teams with a RadGraph ER F1 score of 23.9.


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Evaluation of Medical Concept Annotation Systems on Clinical Records
Hamed Hassanzadeh | Anthony Nguyen | Bevan Koopman
Proceedings of the Australasian Language Technology Association Workshop 2016


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Semantic Judgement of Medical Concepts: Combining Syntagmatic and Paradigmatic Information with the Tensor Encoding Model
Michael Symonds | Guido Zuccon | Bevan Koopman | Peter Bruza | Anthony Nguyen
Proceedings of the Australasian Language Technology Association Workshop 2012