Persona:
Pérez Martín, Jorge

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ORCID
0000-0002-3588-7233
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Pérez Martín
Nombre de pila
Jorge
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2021 - 20245
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Fecha de finalización: 2024 × Fecha de inicio: 2020 ×

Resultados de la búsqueda

Mostrando 1 - 5 de 5
  • Miniatura
    Publicación
    Quality analysis of a breast thermal images database
    (Sage Journals, 2023-02-02) Pérez Martín, Jorge::virtual::28::600; Sánchez Cauce, Raquel; Pérez Martín, Jorge; Pérez Martín, Jorge; Pérez Martín, Jorge; https://orcid.org/0000-0002-1128-3988
    The study and early detection of breast cancer are key for its treatment. We carry out an exhaustive analysis of the most used database for mastology research with infrared images, analyzing the anomalies according to five quality dimensions: completeness, correctness, concordance, plausibility, and currency. We established control queries that looked for these anomalies and that can be used to ensure the quality of the database. Finally, we briefly review the more than 40 papers that use this database and that do not mention any of these anomalies. When analyzing the database, we found 365 anomalies related to personal and clinical data, and thermal images. The errors found in our research may lead to a modification of the results and conclusions made in the articles found in the literature, serve as a basis for improvements in the quality of the database, and help future researchers to work with it.
  • No hay miniatura disponible
    Publicación
    La infraestructura de la calidad para apoyar la contratación pública sostenible
    (2024) Massó Aguado, Daniel; Arbeloa Losada, Marta; García López, Paloma; Pérez Martín, Jorge
  • No hay miniatura disponible
    Publicación
    El papel de los consumidores para alcanzar los ODS
    (2024) Massó Aguado, Daniel; Arbeloa Losada, Marta; García López, Paloma; Pérez Martín, Jorge
  • Miniatura
    Publicación
    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.
  • Miniatura
    Publicación
    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.