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Fecha
2021-08-24
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Asociación Española para la Inteligencia Artificial
Citas
Resumen
Decision analysis networks (DANs) are a new type of probabilistic graphical model. Like influence diagrams (IDs), they are much more compact and easier to build than decision trees and can represent conditional independencies, but they can also represent problems involving partial orderings of the decisions (order asymmetry) and other types of asymmetry. Given that DANs can solve symmetric problems as easily and as efficiently as IDs, and are more appropriate for asymmetric problems—which include virtually all real-world problems—DANs might replace IDs as the standard type of probabilistic graphical model for decision analysis. In particular, they can be use to perform cost-effectiveness analyses (CEAs), which are used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost.
Descripción
This is the author accepted manuscript. Accepted for publication on 15 June 2021 in Proceedings of the II Workshop of Spanish A.I. Research Groups in Biomedicine. XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA-20/21). The final published version is available at https://caepia20-21.uma.es/proceedings.html
Categorías UNESCO
Palabras clave
Decision analysis networks, decision trees, influence diagrams, probabilistic graphical models, medical decision making, cost-effectiveness analysis
Citación
Diez, F. J., Luque, M., Arias, M., & Pérez-Martín, J. (2021, septiembre 25). Cost-effectiveness analysis with decision analysis networks. II Workshop of Spanish A.I. Research Groups in Biomedicine. XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA-20/21), Málaga, Spain.
Centro
Facultad de Ciencias
Departamento
Estadística, Investigación Operativa y Cálculo Numérico