Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi.

Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in understanding 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.

Implementation of a lung cancer screening initiative in HIV-infected subjects.

In this pilot program of low-dose computed tomography (LDCT) for the screening of lung cancer (LC) in a targeted population of people with HIV (PWH), its prevalence was 3.6%; the number needed to screen in order to detect one case of lung cancer was 28, clearly outweighing the risks associated with lung cancer screening. While data from additional cohorts with longitudinal measurements are needed, PWH are a target population for lung cancer screening with LDCT.