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Outputs (83)

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.

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024)
Presentation / Conference Contribution
Wu, S., Li, Y., Zhu, K., Zhang, G., Liang, Y., Ma, K., Xiao, C., Zhang, H., Yang, B., Chen, W., Huang, W., Al Moubayed, N., Fu, J., & Lin, C. (2024, August). SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval. Presented at ACL 2024: Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand

Multi-modal information retrieval (MMIR) is a rapidly evolving field where significant progress has been made through advanced representation learning and cross-modality alignment research, particularly in image-text pairs. However, current benchmark... Read More about SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval.

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.

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.