Staff profile

Matthew is a Postdoctoral Research Associate in the Department of Computer Science at Durham University, researching the application of machine learning to healthcare. His research interests lie in the area of explainable machine learning and how this can be used to improve the quality of, and trust in, machine learning models. His research on applying machine learning to the medical domain is in collaboration with numerous healthcare organisations such as University College London Hospitals NHS Trust, the Northern Care Alliance and Evergreen Life.

Before starting his postdoctoral position, he received a PhD in Computer Science from Durham University where he also studied explainable machine learning for healthcare.

Research Interests

• Machine Learning for Healthcare
• Explainable Machine Learning
• Bias and Fairness in Machine Learning
• Data Shift in (healthcare) Machine Learning

• Explainable Machine Learning, Machine Learning in Healthcare, Bias and Fairness in Machine Learning

Conference Paper

• Watson, M., Awwad Shiekh Hasan, B., & Al Moubayed, N. (2022, January). Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations. Presented at Proc. Winter Conference on Applications of Computer Vision, Waikoloa, HI
• Watson, M., & Al Moubayed, N. (2021, January). Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. Presented at The 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy

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
• 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
• 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
• Watson, M., Awwad Shekh Hasan, B., & Al Moubayed, N. (2022). Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data. Scientific Reports, 12(19899), Article 19899. https://doi.org/10.1038/s41598-022-24356-6
• Alhassan, Z., Watson, M., Budgen, D., Alshammari, R., Alessa, A., & Al Moubayed, N. (2021). Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records. JMIR Medical Informatics, 9(5), Article e25237. https://doi.org/10.2196/25237