Persona: Pérez Martín, Jorge
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Pérez Martín
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Jorge
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Publicación Cost-effectiveness analysis with unordered decisions(Elsevier, 2021-07) Díez Vegas, Francisco Javier; Luque Gallego, Manuel; Arias Calleja, Manuel; Pérez Martín, JorgeIntroduction 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.Publicación Quality of the captions produced by students of an accessibility MOOC using a semi-automatic tool(Springer Nature, 2020-07-05) Pérez Martín, Jorge; Rodríguez Ascaso, Alejandro; Molanes-López, Elisa M.; https://orcid.org/0000-0003-3217-8551Some people have problems accessing multimedia services available on the web. Contributions to accessibility made by end users who lack institutional support in the design, production or deployment of media accommodations should be considered. The aim of our paper is to assess the quality of the captions produced using YouTube by non-professional subtitlers who only received basic training. We also identify potential improvements either in the subtitling tool or in the training resources, which could enhance the quality of the captions. We conducted a study in which 53 participants of a MOOC on digital accessibility used the automatic speech recognition (ASR) feature of YouTube to produce the captions for a video provided by the teaching staff. We assessed the quality of the captions produced by the students and then compared it with the quality of the captions produced by: (a) a human expert and (b) the ASR-based subtitling by YouTube. Students’ errors occurred mainly in the number of characters per line, the speed of the captions, failing to use a new line per participant, and not including sound effects. The course should warn students to use a new line per participant, teach them how to subtitle sound effects, specify the maximum number of characters per line of text, and inform that in some countries such as Spain, captions can be edited but in other countries this may not be possible. Our recommendations for the YouTube editor include improving both the user interface and the ASR, with a view to enhancing ongoing and future research.Publicación Teaching Probabilistic Graphical Models with OpenMarkov(MDPI, 2022-11-30) Díez Vegas, Francisco Javier; Arias Calleja, Manuel; Pérez Martín, Jorge; Luque Gallego, ManuelOpenMarkov 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.Publicación Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms(Springer, 2024) Carrilero Mardones, Mikel; Parras Jurado, Manuela; Nogales, Alberto; Pérez Martín, Jorge; Díez Vegas, Francisco Javier; https://orcid.org/0000-0003-4951-8102Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting and Data System (BI-RADS) to describe tumors according to several features (shape, margin, orientation...) and estimate their malignancy, with a common language. To aid in tumor diagnosis with BI-RADS explanations, this paper presents a deep neural network for tumor detection, description, and classification. An expert radiologist described with BI-RADS terms 749 nodules taken from public datasets. The YOLO detection algorithm is used to obtain Regions of Interest (ROIs), and then a model, based on a multi-class classification architecture, receives as input each ROI and outputs the BI-RADS descriptors, the BI-RADS classification (with 6 categories), and a Boolean classification of malignancy. Six hundred of the nodules were used for 10-fold cross-validation (CV) and 149 for testing. The accuracy of this model was compared with state-of-the-art CNNs for the same task. This model outperforms plain classifiers in the agreement with the expert (Cohen’s kappa), with a mean over the descriptors of 0.58 in CV and 0.64 in testing, while the second best model yielded kappas of 0.55 and 0.59, respectively. Adding YOLO to the model significantly enhances the performance (0.16 in CV and 0.09 in testing). More importantly, training the model with BI-RADS descriptors enables the explainability of the Boolean malignancy classification without reducing accuracy.Publicación Assessment of Video Accessibility by Students of a MOOC on Digital Materials for All(Institute of Electrical and Electronics Engineers, 2021-05-21) Elisa M. Molanes López; Rodríguez Ascaso, Alejandro; Letón Molina, Emilio; Pérez Martín, Jorge; https://orcid.org/0000-0003-3217-8551The assessment of multimedia accessibility is a relevant, complex and time-consuming task, which takes more than simply checking whether the video has audiodescription and captions or not. In our study, we face this challenge through the: 1) involvement of a cohort of novice evaluators, who previously took part in a MOOC on the accessibility of digital content and 2) the division of the accessibility assessment into the application of a set of criteria. Two groups of novice accessibility testers were asked to evaluate the accessibility of two similar videos, one video per group. While both videos were equivalent in terms of their pedagogical content, only one of them had non-severe accessibility barriers for people with low vision and for blind people. Each participant was asked to rate qualitatively a set of statements extracted from the WCAG 2.1 success criteria, one generic statement about the video accessibility, and a set of statements on the quality perception and the aspects of personal preference. The largest differences in ratings occurred for statements whose success criteria had been improved. It was also the case for one success criterion that is understandable but hardly applicable by novice evaluators, according to the literature. However, the difference was statistically significant only for the success criterion with more salient differences between both videos. As a main conclusion, a group of novice evaluators can identify accessibility problems in videos when using specific accessibility statements.Publicación La infraestructura de la calidad para apoyar la contratación pública sostenible(2024) Massó Aguado, Daniel; Arbeloa Losada, Marta; García López, Paloma; Pérez Martín, JorgePublicación El papel de los consumidores para alcanzar los ODS(2024) Massó Aguado, Daniel; Arbeloa Losada, Marta; García López, Paloma; Pérez Martín, JorgePublicación Performance of students with different accessibility needs and preferences in “Design for All” MOOCs(Public Library of Science, 2024-03-07) Rodríguez Ascaso, Alejandro; Molanes López, Elisa M.; Jorge Pe´rez-Martı´nI; Pérez Martín, Jorge; Letón Molina, Emilio; https://orcid.org/0000-0003-3217-8551Recent research has shown that Massive Open Online Courses (MOOCs) create barriers for students with disabilities. Not taking into account their needs in the design, production or delivery of MOOCs may be one of the main causes behind this. It leads to poor compliance with suitable learning designs and web accessibility standards, as well as a lack of knowledge about the students’ needs. The objective of our research is to analyze the learning performance of the students in MOOCs on topics related to Design for All, offered in an Open edX-based platform. Accessibility support was conceived from the outset, including compliance of both the platform and the learning resources with the WCAG 2.1 accessibility standard, and with a subset of the principles of Universal Design for Learning. Additionally, students were consulted on their accessibility needs and preferences, following publicly available modeling schemes and previous research. From a sample of 765 students, who completed at least one of the graded assessment activities of the course, a multilevel multiple logistic regression model was fitted. Based on that model, the results indicate that: a) users of screen readers and users of captions show a statistically significant positive association with a good performance when compared to students with no preferences, with an odds ratio of, respectively, OR = 13.482 and OR = 13.701; b) students who have low vision or very low vision show a significant negative association with a good performance when compared to users of screen readers and to users of captions, with OR = 26.817 and OR = 27.254, respectively.Publicación Quality analysis of a breast thermal images database(Sage Journals, 2023-02-02) Sánchez Cauce, Raquel; Pérez Martín, Jorge; https://orcid.org/0000-0002-1128-3988The 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.