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Machine learning models are increasingly used in the medical domain to studythe association between risk factors and diseases to support practitioners inpredicting health outcomes. In this paper, we showcase the use ofmachine-learned staged tree models for investigating complex asymmetricdependence structures in health data. Staged trees are a specific class ofgenerative, probabilistic graphical models that formally model asymmetricconditional independence and non-regular sample spaces. An investigation of therisk factors in invasive fungal infections demonstrates the insights stagedtrees provide to support medical decision-making.

Using Staged Tree Models for Health Data: Investigating Invasive Fungal Infections by Aspergillus and Other Filamentous Fungi