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

TL;DR

This paper demonstrates how staged tree models, a type of probabilistic graphical model, can uncover complex asymmetric dependencies in health data, aiding medical decision-making for invasive fungal infections.

Contribution

It introduces the application of machine-learned staged tree models to health data, highlighting their ability to model asymmetric dependencies and improve understanding of risk factors.

Findings

Staged trees reveal complex dependence structures in health data.

Application to fungal infections provides new insights for medical decisions.

Models support better understanding of risk factors in invasive fungal infections.

Abstract

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