Speaker
Description
With the increasing volume and complexity of data, machine learning (ML) models are becoming indispensable tools for identifying patterns within structured datasets. However, as these models grow in complexity, it becomes challenging to determine whether they are truly learning meaningful relationships or capturing unintended artifacts. This lack of interpretability leads to mistrust, particularly in high-stakes domains. In clinical settings, prognostic models must be fully explainable to clinicians and patients to ensure confidence in medical decisions. For example, age-related macular degeneration (AMD) is the leading cause of legal blindness in developed countries, and current existing methods of predicting disease progression (survival modelling) can often lack interpretable explanations of progression and can misclassify progressing cases as non-progressing in unbalanced datasets. Similarly, in particle physics, ML-based classification models must be demonstrably aligned with known physical principles, such as those described by the Standard Model. Here, we explore how a Graph Neural Network (GNN) can be adapted from the identification of hadronic tau decays developed for ATLAS to a survival model to predict the risk of disease progression in AMD patients, ensuring robust and interpretable ML applications across disciplines.