Examinando por Autor "Arias Calleja, Manuel"

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    Carmen : una herramienta de software libre para modelos gráficos probabilistas
    (Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial, 2009-10-22) Arias Calleja, Manuel; Díez Vegas, Francisco Javier
    En las últimas dos décadas se ha dado una proliferación de herramientas para la construcción, manual o automática de Modelos Gráficos Probabilistas (MGPs). Las herramientas disponibles están limitadas en su mantenibilidad, robustez y eficiencia. Nuestra contribución principal es una nueva herramienta, llamada Carmen, que se ha desarrollado desde cero y está basada en los principios de la ingeniería del software. Carmen tiene un diseño detallado, una documentación y un conjunto de pruebas sistemáticas para minimizar la presencia de errores. El desarrollo de esta herramienta ha traido como consecuencia varias contribuciones secundarias: primero, un nuevo patrón de diseño llamado permiso-ejecución, que permite realizar operaciones en modelos complejos con múltiples restricciones; segundo, hemos desarrollado un nuevo diseño, que desacopla los diferentes conceptos que constituyen un MGP en partes distintas, permitiendo un mantenimiento posterior más sencillo; tercero, hemos desarrollado una librería genérica de grafos que puede ser utilizada en otras herramientas. Nuestra segunda contribución principal es un método nuevo que mejora significativamente el rendimiento en las operaciones básicas sobre potenciales de variables discretas, tales como suma, multiplicación, marginalización y división. Hemos demostrado también, tanto teórica como empíricamente, que algunas operaciones compuestas pueden ser realizadas de un modo mucho más eficiente si se ejecutan de forma conjunta en lugar de secuencial. Esta mejora en las operaciones de bajo nivel nos lleva a una reducción en el tiempo y en el espacio necesarios en algoritmos de alto nivel, tales como eliminación de variables, propagación en árboles de cliques, etc. Finalmente, la tercera contribución principal es un nuevo método para el análisis de coste-efectividad. Los métodos actuales no pueden tratar con problemas que involucran más de una decisión. Por este motivo, hemos desarrollado un nuevo método de coste-efectividad, que puede ser aplicado tanto en árboles de decisión como en diagramas de influencia. Nuestro método es capaz de manejar varias decisiones y devuelve la estrategia óptima como un conjunto de intervalos para λ, un parámetro habitualmente llamado disponibilidad a pagar, que representa la cantidad de dinero equivalente a una unidad de efectividad.
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    Cost-effectiveness analysis with unordered decisions
    (Elsevier, 2021-07) Díez Vegas, Francisco Javier; Luque Gallego, Manuel; Arias Calleja, Manuel; Pérez Martín, Jorge
    Introduction Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEAs for very small problems. Influence diagrams can model much larger problems, but only when the decisions are totally ordered. Objective To develop a CEA method for problems with unordered or partially ordered decisions, such as finding the optimal sequence of tests for diagnosing a disease. Methods We explain how to model those problems using decision analysis networks (DANs), a new type of probabilistic graphical model, somewhat similar to Bayesian networks and influence diagrams. We present an algorithm for evaluating DANs with two criteria, cost and effectiveness, and perform some experiments to study its computational efficiency. We illustrate the representation framework and the algorithm using a hypothetical example involving two therapies and several tests and then present a DAN for a real-world problem, the mediastinal staging of non-small cell lung cancer. Results The evaluation of a DAN with two criteria, cost and effectiveness, returns a set of intervals for the willingness to pay, separated by incremental cost-effectiveness ratios (ICERs). The cost, the effectiveness, and the optimal intervention are specific for each interval, i.e., they depend on the willingness to pay. Conclusion Problems involving several unordered decisions can be modeled with DANs and evaluated in a reasonable amount of time. OpenMarkov, an open-source software tool developed by our research group, can be used to build the models and evaluate them using a graphical user interface.
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    Markov influence diagrams: a graphical tool for cost-effectiveness analysis
    (Society for Medical Decision Making, 2017-01-11) Yebra, Mar; Bermejo, Iñigo; Palacios Alonso, Miguel Ángel; Arias Calleja, Manuel; Luque Gallego, Manuel; Pérez Martín, Jorge; Díez Vegas, Francisco Javier
    Markov influence diagrams (MIDs) are a new type of probabilistic graphical models that extend influence diagrams in the same way as Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analysis. Using a causal graph that may contain several variables per cycle, MIDs can model various features of the patient without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs—including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis—with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs, i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.
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    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.
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    Optimización de árboles de estrategia unicriterio y de coste-efectividad
    (Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial, 2020-10-02) Gil González, Ángel Miguel; Arias Calleja, Manuel; Luque Gallego, Manuel; Díez Vegas, Francisco Javier
    Introduction: A cost-effectiveness analysis (CEA) helps us select the most effective intervention for the financial budget we have. In these analyzes through CEP, we generated several lambda intervals, each with the optimal intervention, its cost and expected effectiveness. Being able to represent these lambda intervals, through a graphic model, will help us see all the available information and will speed us up to make a decision. If we can optimize this tree, it will be easier to study and understand it. Objective: To find the algorithm that returns the best optimization of the generated tree after applying the deterministic CEA analysis. Methods: Applying pruning techniques on the tree nodes (variable exchange, elimination of redundancies, lambda displacement) in a search algorithm, we will be able to get partial results of the search optimization, until we reach the most optimal tree optimization result created with CEP results. Results: Once optimized with the most appropriate pruning techniques and algorithms, we will have a graphic model where the different options can be observed in a more effective and clear way than when we use a textual description. Conclusion: Through the open source software tool "OpenMarkov" the possibility of graphically displaying the result of a deterministic CEA analysis has been implemented. With this new tool we can graphically evaluate the results that were previously shown in a table, and that did not allow us to appreciate in the same detail, each of the concepts that are observed in each branch of the tree.
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    Teaching Probabilistic Graphical Models with OpenMarkov
    (MDPI, 2022-11-30) Díez Vegas, Francisco Javier; Arias Calleja, Manuel; Pérez Martín, Jorge; Luque Gallego, Manuel
    OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to use it as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, such as conditional dependence and independence, d-separation, Markov blankets, explaining away, optimal policies, expected utilities, etc., and some inference algorithms: logic sampling, likelihood weighting, and arc reversal. The facilities for learning Bayesian networks interactively can be used to illustrate step by step the performance of the two basic algorithms: search-and-score and PC.