Persona:
Pérez Martín, Jorge

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jperezmartin@dia.uned.es
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0000-0002-3588-7233
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Pérez Martín
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Jorge
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2019 - 201912020 - 20212
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Tipo: conference proceedings ×

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Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models
    (International Joint Conference on Artificial Intelligence, 2019) Arias Calleja, Manuel; Pérez Martín, Jorge; Luque Gallego, Manuel; Díez Vegas, Francisco Javier
    OpenMarkov is a Java open-source tool for creating and evaluating probabilistic graphical models, including Bayesian networks, influence diagrams, and some Markov models. With more than 100,000 lines of code, it offers some features for interactive learning, explanation of reasoning, and cost-effectiveness analysis, which are not available in any other tool. OpenMarkov has been used at universities, research centers, and large companies in more than 30 countries on four continents. Several models, some of them for real-world medical applications, built with OpenMarkov, are publicly available on Internet.
  • Miniatura
    Publicación
    Cost-effectiveness analysis with probabilistic graphical models
    (Bayesian Network Modelling Association, 2021-06-30) Díez Vegas, Francisco Javier; Luque Gallego, Manuel; Arias Calleja, Manuel; Pérez Martín, Jorge
    The two formalisms most widely used for the representation and analysis of decision problems in medicine are decision trees and Markov flat models, which seriously limit the complexity of the problems that can be addressed. In contrast, probabilistic graphical models (PGMs) can represent the state and the evolution of the system (the patient) using much richer structures, but they are rarely used for economic evaluations in medicine given that, until recently, they could not perform cost-effectiveness analysis (CEA). In this paper we summarize the research done by our group, developing new types of PGMs and new algorithms for CEA and implementing them in OpenMarkov, an open-source tool especially designed for medicine.
  • Miniatura
    Publicación
    Cost-effectiveness analysis with decision analysis networks
    (Asociación Española para la Inteligencia Artificial, 2021-08-24) Díez Vegas, Francisco Javier; Luque Gallego, Manuel; Arias Calleja, Manuel; Pérez Martín, Jorge; Agencia Estatal de Investigación (España)
    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.