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 depe...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | IE |
| Repositorio: | Repositorio IE |
| OAI Identifier: | oai:repositorio.ie.edu:20.500.14417/3914 |
| Acceso en línea: | https://doi.org/10.1016/j.csbj.2023.11.013 https://hdl.handle.net/20.500.14417/3914 https://www.sciencedirect.com/science/article/pii/S2001037023004282 |
| Access Level: | acceso abierto |
| Palabra clave: | Diagnostic criteria Invasive aspergillosis Machine learning Probabilistic graphical models Staged trees 33 Ciencias Tecnológicas ODS 3 - Salud y bienestar |
| Sumario: | 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. |
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