All Outputs (76)

The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information (2025)
Journal Article
Holland, M., Kellett, J., Boulitsakis-Logothetis, S., Watson, M., Al Moubayed, N., & Green, D. (online). The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information. Internal and Emergency Medicine, https://doi.org/10.1007/s11739-024-03828-9

Background: Patients with an elevated admission National Early Warning Score (NEWS) are more likely to die while in hospital. However, it is not known if this increased mortality risk is the same for all diagnoses. The aim of this study was to determ... Read More about The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information.

Racial Bias within Face Recognition: A Survey (2024)
Journal Article
Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. P. (2025). Racial Bias within Face Recognition: A Survey. ACM Computing Surveys, 57(4), 1-39. https://doi.org/10.1145/3705295

Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally. This widespread adoption has uncovered significant performance variati... Read More about Racial Bias within Face Recognition: A Survey.

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions (2024)
Journal Article
Watson, M., Boulitsakis Logothetis, S., Green, D., Holland, M., Chambers, P., & Al Moubayed, N. (2024). Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions. BMJ Health & Care Informatics, 31(1), Article e101088. https://doi.org/10.1136/bmjhci-2024-101088

Objectives: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe... Read More about Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities (2024)
Journal Article
Farrell, S., Anderson, K., Noble, P.-J. M., & Al Moubayed, N. (2024). Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities. Scientific Reports, 14(1), Article 28763. https://doi.org/10.1038/s41598-024-77385-8

Monitoring mortality rates offers crucial insights into public health by uncovering the hidden impacts of diseases, identifying emerging trends, optimising resource allocation, and informing effective policy decisions. Here, we present a novel approa... Read More about Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities.

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers (2024)
Journal Article
Watson, M., Chambers, P., Steventon, L., Harmsworth King, J., Ercia, A., Shaw, H., & Al Moubayed, N. (2024). From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology, 3(1), Article e000430. https://doi.org/10.1136/bmjonc-2024-000430

Objectives: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model... Read More about From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text (2024)
Journal Article
Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S., Radford, A. D., & Noble, P.-J. M. (2024). Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text. Frontiers in Veterinary Science, 11, Article 1352726. https://doi.org/10.3389/fvets.2024.1352726

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clini... Read More about Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text.

Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics (2024)
Presentation / Conference Contribution
Yucer, S., Abarghouei, A. A., Al Moubayed, N., & Breckon, T. P. (2024, June). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. Presented at 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges... Read More about Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics.

Explainable text-tabular models for predicting mortality risk in companion animals (2024)
Journal Article
Burton, J., Farrell, S., Mäntylä Noble, P.-J., & Al Moubayed, N. (2024). Explainable text-tabular models for predicting mortality risk in companion animals. Scientific Reports, 14(1), Article 14217. https://doi.org/10.1038/s41598-024-64551-1

As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets contai... Read More about Explainable text-tabular models for predicting mortality risk in companion animals.

CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs (2024)
Presentation / Conference Contribution
Shentu, J., & Al Moubayed, N. (2024, January). CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs. Presented at 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, USA

Chest X-Ray (CXR) images play a crucial role in clinical practice, providing vital support for diagnosis and treatment. Augmenting the CXR dataset with synthetically generated CXR images annotated with radiology reports can enhance the performance of... Read More about CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs.

Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words (2024)
Journal Article
Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S. O., …Noble, P. M. (2024). Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words. Frontiers in Veterinary Science, 11, Article 1352239. https://doi.org/10.3389/fvets.2024.1352239

The development of natural language processing techniques for deriving useful information from unstructured clinical narratives is a fast-paced and rapidly evolving area of machine learning research. Large volumes of veterinary clinical narratives no... Read More about Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words.

On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning (2023)
Presentation / Conference Contribution
Xiao, C., Long, Y., & Al Moubayed, N. (2023, July). On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning. Presented at Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada

Length is a Curse and a Blessing for Document-level Semantics (2023)
Presentation / Conference Contribution
Xiao, C., Li, Y., Hudson, G. T., Lin, C., & Al Moubayed, N. (2023, December). Length is a Curse and a Blessing for Document-level Semantics. Presented at The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore

In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models. In this work, we question the length generalizability of CL-based models, i.e., thei... Read More about Length is a Curse and a Blessing for Document-level Semantics.

PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records (2023)
Journal Article
Farrell, S., Appleton, C., Noble, P. M., & Al Moubayed, N. (2023). PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records. Scientific Reports, 13(1), Article 18015. https://doi.org/10.1038/s41598-023-45155-7

Effective public health surveillance requires consistent monitoring of disease signals such that researchers and decision-makers can react dynamically to changes in disease occurrence. However, whilst surveillance initiatives exist in production anim... Read More about PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records.

Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function (2023)
Journal Article
Chambers, P., Watson, M., Bridgewater, J., Forster, M. D., Roylance, R., Burgoyne, R., Masento, S., Steventon, L., Harmsworth King, J., Duncan, N., & al Moubayed, N. (2023). Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Medicine, 12(17), 17856-17865. https://doi.org/10.1002/cam4.6418

Background
In those receiving chemotherapy, renal and hepatic dysfunction can increase the risk of toxicity and should therefore be monitored. We aimed to develop a machine learning model to identify those patients that need closer monitoring, enabl... Read More about Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.

Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making (2023)
Journal Article
Boulitsakis Logothetis, S., Green, D., Holland, M., & Al Moubayed, N. (2023). Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making. Scientific Reports, 13(1), Article 13563. https://doi.org/10.1038/s41598-023-40661-0

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent ris... Read More about Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making.

Addressing Performance Inconsistency in Domain Generalization for Image Classification (2023)
Presentation / Conference Contribution
Stirling, J., & Moubayed, N. A. (2023, June). Addressing Performance Inconsistency in Domain Generalization for Image Classification. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia

Domain Generalization (DG) in computer vision aims to replicate the human ability to generalize well under a shift of data distribution, or domain. In recent years, the field of domain generalization has seen a steady increase in average left-out tes... Read More about Addressing Performance Inconsistency in Domain Generalization for Image Classification.

Natural Language Explanations for Machine Learning Classification Decisions (2023)
Presentation / Conference Contribution
Burton, J., Al Moubayed, N., & Enshaei, A. (2023, June). Natural Language Explanations for Machine Learning Classification Decisions. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia

This paper addresses the challenge of providing understandable explanations for machine learning classification decisions. To do this, we introduce a dataset of expert-written textual explanations paired with numerical explanations, forming a data-to... Read More about Natural Language Explanations for Machine Learning Classification Decisions.

Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation (2023)
Journal Article
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence... Read More about Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation.

Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data (2023)
Journal Article
Kluvanec, D., McCaffrey, K. J., Phillips, T. B., & Al Moubayed, N. (2023). Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data. IEEE Transactions on Geoscience and Remote Sensing, 61, https://doi.org/10.1109/tgrs.2023.3273329

We can represent the orientation of a plane in 3D by its normal vector. However, every plane has two normal vectors that are negatives of each other. We propose four novel representations of vectors in 3D that are negation invariant and can be used b... Read More about Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data.

Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022, December). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. Presented at IEEE Big Data, Osaka, Japan

Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio formed of questions, multimodal features and expert knowledge. In this... Read More about Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention.