2024
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e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation
Aaron Nicolson
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Jinghui Liu
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Jason Dowling
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Anthony Nguyen
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Bevan Koopman
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
The core novelty of our approach lies in the addition of entropy regularisation to self-critical sequence training. This helps maintain a higher entropy in the token distribution, preventing overfitting to common phrases and ensuring a broader exploration of the vocabulary during training, which is essential for handling the diversity of the radiology reports in the RRG24 datasets. We apply this to a multimodal language model with RadGraph as the reward. Additionally, our model incorporates several other aspects. We use token type embeddings to differentiate between findings and impression section tokens, as well as image embeddings. To handle missing sections, we employ special tokens. We also utilise an attention mask with non-causal masking for the image embeddings and a causal mask for the report token embeddings.
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e-Health CSIRO at “Discharge Me!” 2024: Generating Discharge Summary Sections with Fine-tuned Language Models
Jinghui Liu
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Aaron Nicolson
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Jason Dowling
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Bevan Koopman
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Anthony Nguyen
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Clinical documentation is an important aspect of clinicians’ daily work and often demands a significant amount of time. The BioNLP 2024 Shared Task on Streamlining Discharge Documentation (Discharge Me!) aims to alleviate this documentation burden by automatically generating discharge summary sections, including brief hospital course and discharge instruction, which are often time-consuming to synthesize and write manually. We approach the generation task by fine-tuning multiple open-sourced language models (LMs), including both decoder-only and encoder-decoder LMs, with various configurations on input context. We also examine different setups for decoding algorithms, model ensembling or merging, and model specialization. Our results show that conditioning on the content of discharge summary prior to the target sections is effective for the generation task. Furthermore, we find that smaller encoder-decoder LMs can work as well or even slightly better than larger decoder-based LMs fine-tuned through LoRA. The model checkpoints from our team (aehrc) are openly available.
2023
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Catching Misdiagnosed Limb Fractures in the Emergency Department Using Cross-institution Transfer Learning
Filip Rusak
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Bevan Koopman
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Nathan J. Brown
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Kevin Chu
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Jinghui Liu
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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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Enhancing Bacterial Infection Prediction in Critically Ill Patients by Integrating Clinical Text
Jinghui Liu
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Anthony Nguyen
Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
Bacterial infection (BI) is an important clinical condition and is related to many diseases that are difficult to treat. Early prediction of BI can lead to better treatment and appropriate use of antimicrobial medications. In this paper, we study a variety of NLP models to predict BI for critically ill patients and compare them with a strong baseline based on clinical measurements. We find that choosing the proper text-based model to combine with measurements can lead to substantial improvements. Our results show the value of clinical text in predicting and managing BI. We also find that the NLP model developed using patients with BI can be transferred to the more general patient cohort for patient risk prediction.
2022
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Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text
Jinghui Liu
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Daniel Capurro
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Anthony Nguyen
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Karin Verspoor
Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association
2019
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Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning
Thanh Vu
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Anthony Nguyen
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Nathan Brown
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James Hughes
Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association
Pain is the main symptom that patients present with to the emergency department (ED). Pain management, however, is often poorly done aspect of emergency care and patients with painful conditions can endure long waits before their pain is assessed or treated. To improve pain management quality, identifying whether or not an ED patient presents with pain is an important task and allows for further investigation of the quality of care provided. In this paper, machine learning was utilised to handle the task of automatically detecting patients who present at EDs with pain from retrospective data. Experimental results on a manually annotated dataset show that our proposed machine learning models achieve high performances, in which the highest accuracy and macro-averaged F1 are 91.00% and 90.96%, respectively.
2018
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Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach
Thanat Chokwijitkul
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Anthony Nguyen
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Hamed Hassanzadeh
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Siegfried Perez
Proceedings of the BioNLP 2018 workshop
Automatic identification of heart disease risk factors in clinical narratives can expedite disease progression modelling and support clinical decisions. Existing practical solutions for cardiovascular risk detection are mostly hybrid systems entailing the integration of knowledge-driven and data-driven methods, relying on dictionaries, rules and machine learning methods that require a substantial amount of human effort. This paper proposes a comparative analysis on the applicability of deep learning, a re-emerged data-driven technique, in the context of clinical text classification. Various deep learning architectures were devised and evaluated for extracting heart disease risk factors from clinical documents. The data provided for the 2014 i2b2/UTHealth shared task focusing on identifying risk factors for heart disease was used for system development and evaluation. Results have shown that a relatively simple deep learning model can achieve a high micro-averaged F-measure of 0.9081, which is comparable to the best systems from the shared task. This is highly encouraging given the simplicity of the deep learning approach compared to the heavily feature-engineered hybrid approaches that were required to achieve state-of-the-art performances.
2017
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Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Sarvnaz Karimi
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Xiang Dai
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Hamed Hassanzadeh
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Anthony Nguyen
BioNLP 2017
Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.
2016
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Evaluation of Medical Concept Annotation Systems on Clinical Records
Hamed Hassanzadeh
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Anthony Nguyen
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Bevan Koopman
Proceedings of the Australasian Language Technology Association Workshop 2016
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The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction
Mahnoosh Kholghi
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Lance De Vine
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Laurianne Sitbon
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Guido Zuccon
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Anthony Nguyen
Proceedings of the Australasian Language Technology Association Workshop 2016
2015
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Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction
Lance De Vine
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Mahnoosh Kholghi
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Guido Zuccon
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Laurianne Sitbon
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Anthony Nguyen
Proceedings of the Australasian Language Technology Association Workshop 2015
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Similarity Metrics for Clustering PubMed Abstracts for Evidence Based Medicine
Hamed Hassanzadeh
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Diego Mollá
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Tudor Groza
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Anthony Nguyen
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Jane Hunter
Proceedings of the Australasian Language Technology Association Workshop 2015
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UQeResearch: Semantic Textual Similarity Quantification
Hamed Hassanzadeh
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Tudor Groza
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Anthony Nguyen
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Jane Hunter
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
2012
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Semantic Judgement of Medical Concepts: Combining Syntagmatic and Paradigmatic Information with the Tensor Encoding Model
Michael Symonds
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Guido Zuccon
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Bevan Koopman
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Peter Bruza
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Anthony Nguyen
Proceedings of the Australasian Language Technology Association Workshop 2012