Análisis de sensibilidad en redes Bayesianas Gaussianas

Al construir una Red Bayesiana se requiere que los expertos en el campo de aplicación especifiquen las dependencias entre las variables del problema e indiquen los parámetros que describen la red. Mediante este proceso de diseño y definición frecuentemente pueden asignarse erróneamente algunos parámetros y obtener resultados inadecuados. Por ello, surge la necesidad de introducir una medida de sensibilidad y robustez para Redes Bayesianas. En la bibliografía existente se han desarrollado diversas técnicas con este objetivo aunque los análisis disponibles estudian redes discretas o pequeños cambios alrededor de los parámetros. En esta memoria se desarrolla una metodología basada en una medida de discrepancia entre dos modelos de probabilidad; el inicial y el perturbado. La medida está basada en la seudodistancia de Kullback-Leibler y mediante ella, se compara la salida de la red para los dos modelos, con el fin de estudiar la sensibilidad o la robustez de una Red Bayesiana Gaussiana cuando se producen distintos cambios, pequeños o grandes, en los parámetros inciertos que describen la red. La metodología propuesta se concreta en el desarrollo de tres tipos de análisis para Redes Bayesianas Gaussianas, lo que en la memoria se denominan: análisis de sensibilidad de una vía, análisis de sensibilidad de n vías y análisis de robustez. Con los resultados obtenidos es posible determinar el comportamiento de una Red Bayesiana Gaussiana frente a todos los tipos de perturbaciones asociadas a los parámetros inciertos de la red

Desarrollo de herramientas on line para estudiar la inserción laboral de los graduados de la Universidad Complutense

Depto. de Biodiversidad, Ecología y EvoluciónFac. de Ciencias BiológicasFALSEsubmitte

Definición y estudios de redes bayesianas aplicadas a ciencias de la salud y de la vida

Las redes bayesianas son modelos gráficos probabilísticos que expresan las relaciones de dependencia condicional en un conjunto de variables. Desde su concepción, las redes bayesianas han estado profundamente ligadas a las Ciencias de la Salud y de la Vida, especialmente en el área clínica. Existe una bibliografía extensa sobre aplicaciones de las redes bayesianas a este ámbito. Sin embargo, el análisis de algoritmos de aprendizaje de redes y parámetros, y su aptitud en función de factores como la cantidad de variables, la naturaleza de los datos o la complejidad de la estructura de dependencia no es un tema común en la literatura. En este trabajo, analizamos la aplicación de estas técnicas a problemas descritos en la bibliografía, exploramos el software bnlearn disponible en el lenguaje de programación R documentando nuestro código y evaluamos las estrategias de aprendizaje que mejor se ajustan a cada tipo de datos. Esperamos con ello aportar conocimiento sobre las redes bayesianas y proporcionar un punto de partida para su estudio a profesionales sanitarios e investigadores

The Computer-Vision Symptom Scale (CVSS17): Development and Initial Validation

Purpose.: To develop a questionnaire (in Spanish) to measure computer-related visual and ocular symptoms (CRVOS). Methods.: A pilot questionnaire was created by consulting the literature, clinicians, and video display terminal (VDT) workers. The replies of 636 subjects completing the questionnaire were assessed using the Rasch model and conventional statistics to generate a new scale, designated the Computer-Vision Symptom Scale (CVSS17). Validity and reliability were determined by Rasch fit statistics, principal components analysis (PCA), person separation, differential item functioning (DIF), and item–person targeting. To assess construct validity, the CVSS17 was correlated with a Rasch-based visual discomfort scale (VDS) in 163 VDT workers, this group completed the CVSS17 twice in order to assess test-retest reliability (two-way single-measure intraclass correlation coefficient [ICC] and their 95% confidence intervals, and the coefficient of repeatability [COR]). Results.: The CVSS17 contains 17 items exploring 15 different symptoms. These items showed good reliability and internal consistency (mean square infit and outfit 0.88–1.17, eigenvalue for the first residual PCA component 1.37, person separation 2.85, and no DIF). Pearson's correlation with VDS scores was 0.60 (P < 0.001). Intraclass correlation coefficient for test–retest reliability was 0.849 (95% confidence interval [CI], 0.800–0.887), and COR was 8.14. Conclusions.: The Rasch-based linear-scale CVSS17 emerged as a useful tool to quantify CRVOS in computer workers

Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis

Purpose: To quantify the levels of performance (symptom severity) of the computer-vision symptom scale (CVSS17), confirm its bifactorial structure as detected in an exploratory factor analysis, and validate its factors as subscales. Methods: By partial credit model (PCM), we estimated CVSS17 measures and the standard error for every possible raw score, and used these data to determine the number of different performance levels in the CVSS17. In addition, through discriminant analysis, we checked that the scale's two main factors could classify subjects according to these determined levels of performance. Finally, a separate Rasch analysis was performed for each CVSS17 factor to assess their measurement properties when used as isolated scales. Results: We identified 5.8 different levels of performance. Discriminant functions obtained from sample data indicated that the scale's main factors correctly classified 98.4% of the cases. The main factors: Internal symptom factor (ISF) and external symptom factor (ESF) showed good measurement properties and can be considered as subscales. Conclusion: CVSS17 scores defined five different levels of performance. In addition, two main factors (ESF and ISF) were identified and these confirmed by discriminant analysis. These subscales served to assess either the visual or the ocular symptoms attributable to computer use

Bayesian reasoning with emotional material in patients with schizophrenia.

Delusions are one of the most classical symptoms described in schizophrenia. However, despite delusions are often emotionally charged, they have been investigated using tasks involving non-affective material, such as the Beads task. In this study we compared 30 patients with schizophrenia experiencing delusions with 32 matched controls in their pattern of responses to two versions of the Beads task within a Bayesian framework. The two versions of the Beads task consisted of one emotional and one neutral, both with ratios of beads of 60:40 and 80:20, considered, respectively, as the “difficult” and “easy” variants of the task. Results indicate that patients showed a greater deviation from the normative model, especially in the 60:40 ratio, suggesting that more inaccurate probability estimations are more likely to occur under uncertainty conditions. Additionally, both patients and controls showed a greater deviation in the emotional version of the task, providing evidence of a reasoning bias modulated by the content of the stimuli. Finally, a positive correlation between patients’ deviation and delusional symptomatology was found. Impairments in the 60:40 ratio with emotional content was related to the amount of disruption in life caused by delusions. These results contribute to the understanding of how cognitive mechanisms interact with characteristics of the task (i.e., ambiguity and content) in the context of delusional thinking. These findings might be used to inform improved intervention programs in the domain of inferential reasoning.post-print700 K

I Jornadas de Estudiantes de Ciencias de la Documentación: Compartiendo Conocimiento

El proyecto de innovación que fue presentado al Vicerrectorado de Calidad en el curso 2016-2017, pretendía contribuir al desarrollo de las competencias transversales que los alumnos deben adquirir para su futuro profesional y científico; por tanto, estaba dirigido, a los alumnos de Grado, Máster y Doctorado de nuestra facultad. Las competencias y habilidades asociadas serían: Gestión y organización de eventos, contactos con expertos, investigación sobre temas relacionados con la profesión, presentación de comunicaciones, intercambio de conocimiento y discusión científica

Gamificación en el aula con Kahoot! en la Facultad de Estudios Estadísticos

Memoria final del proyecto de innovación docente titulado: "Gamificación en el aula con Kahoot! en la Facultad de Estudios Estadísticos

Causes of death in women with breast cancer: a risks and rates study on a population-based cohort

IntroductionThe increasing survival of patients with breast cancer has prompted the assessment of mortality due to all causes of death in these patients. We estimated the absolute risks of death from different causes, useful for health-care planning and clinical prediction, as well as cause-specific hazards, useful for hypothesis generation on etiology and risk factors.Materials and methodsUsing data from population-based cancer registries we performed a retrospective study on a cohort of women diagnosed with primary breast cancer. We carried out a competing-cause analysis computing cumulative incidence functions (CIFs) and cause-specific hazards (CSHs) in the whole cohort, separately by age, stage and registry area.ResultsThe study cohort comprised 12,742 women followed up for six years. Breast cancer showed the highest CIF, 13.71%, and cardiovascular disease was the second leading cause of death with a CIF of 3.60%. The contribution of breast cancer deaths to the CIF for all causes varied widely by age class: 89.25% in women diagnosed at age &lt;50 years, 72.94% in women diagnosed at age 50–69 and 48.25% in women diagnosed at age ≥70. Greater CIF variations were observed according to stage: the contribution of causes other than breast cancer to CIF for all causes was 73.4% in women with stage I disease, 42.9% in stage II–III and only 13.2% in stage IV. CSH computation revealed temporal variations: in women diagnosed at age ≥70 the CSH for breast cancer was equaled by that for cardiovascular disease and “other diseases” in the sixth year following diagnosis, and an early peak for breast cancer was identified in the first year following diagnosis. Among women aged 50–69 we identified an early peak for breast cancer followed by a further peak near the second year of follow-up. Comparison by geographic area highlighted conspicuous variations: the highest CIF for cardiovascular disease was more than 70% higher than the lowest, while for breast cancer the highest CIF doubled the lowest.ConclusionThe integrated interpretation of absolute risks and hazards suggests the need for multidisciplinary surveillance and prevention using community-based, holistic and well-coordinated survivorship care models

Variables psicológicas implicadas en la actitud e iniciativa emprendedora (II): personalidad, cognición y emoción

El proyecto titulado: Variables implicadas en la actitud e iniciativa emprendedora (II): personalidad, cognición y emoción, es la continuidad de otro presentado en la convocatoria anterior (2016-2017) cuyo objetivo era evaluar variables psicológicas en la actitud emprendedora de los estudiantes universitarios de la Universidad Complutense de Madrid (UCM). Este segundo proyecto ha tenido por objetivo principal ampliar la evaluación a otras facultades y áreas de conocimiento de nuestra universidad a fin de obtener el mapa y perfil de la iniciativa emprendedora del universitario UCM