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to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a centralised server. Because data never leaves from user clients
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responses over time. The AI model will be then used for providing personalised recommendations by rating food items as poor or healthy choices. Such regular and suggestive feedback using the strategy of
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Primary supervisor David Dowe Co-supervisors Michael Kamp Research area Machine Learning Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC
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Support The research will investigate Explainable AI (XAI) to identify important clinical predictors and improve the interpretability of model predictions. Multimodal and temporal modelling approaches
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Primary supervisor David Dowe Co-supervisors Michael Kamp Research area Machine Learning Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC
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Support The research will investigate Explainable AI (XAI) to identify important clinical predictors and improve the interpretability of model predictions. Multimodal and temporal modelling approaches
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to Do This project is not about building a standard disease-diagnosis model or simply improving classification accuracy on medical datasets. It is also not focused on replacing doctors, giving
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to Do This project is not about building a standard disease-diagnosis model or simply improving classification accuracy on medical datasets. It is also not focused on replacing doctors, giving
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specialised as-needed via fine-tuning or prompt engineering. In this project we will explore all aspects of this process with a focus on increasing trust in the model outputs by reducing or eliminating
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clinical predictors and improve the transparency of model predictions. The research will consider model robustness, generalisability, fairness, uncertainty and responsible use of ML in healthcare