Outputs (5)

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., …al Moubayed, N. (2023). Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Medicine, 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.

Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data (2022)
Journal Article
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

It has been shown that identical Deep Learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and... Read More about Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data.

Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations (2022)
Conference Proceeding
Watson, M., Awwad Shiekh Hasan, B., & Al Moubayed, N. (2022). Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations. . https://doi.org/10.1109/wacv51458.2022.00159

Deep Learning of neural networks has progressively become more prominent in healthcare with models reaching, or even surpassing, expert accuracy levels. However, these success stories are tainted by concerning reports on the lack of model transparenc... Read More about Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations.

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning (2021)
Conference Proceeding
Watson, M., & Al Moubayed, N. (2021). Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. . https://doi.org/10.1109/icpr48806.2021.9412560

Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks h... Read More about Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning.

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records (2021)
Journal Article
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

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive mode... Read More about Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records.