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The evaluation of a medical diagnosis system can depend on several external parameters, such as experts' opinions/criteria or the gold standard used. In addition, there are other parameters that can be measured in a medical diagnosis system, and one of these parameters in particular is the sensitivity. Sensitivity allows knowing how sensible a system is to produce results in different environments. Hence, the aim of this paper is to provide researchers with an index able to estimate a parameter very similar to common sensitivity. This would permit to know an estimation of the results relying on the modeling of the knowledge base. It would be the mathematical justification of this index that would allow estimating the aforementioned parameter. Therefore, the index would in general allow an estimation of the sensitivity without the necessity of having external feedback from experts in the field, which is one of the main lacks within the classical sensitivity metric.

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.

Objective, to determine the features and use of groups related to healthy eating on Facebook. Method, we carried out a cross-sectional study through the Internet. Using the API on Facebook, we included open groups related to healthy eating in the Spanish language. The variables studied were name, description, category, the number and gender of users, date of creation, number of posts, content of the first 20 posts, and the most recent update. Results, we selected 281 open groups for inclusion in the study. Of these, 125 were excluded because the content was unrelated to healthy eating. Finally 156 groups were studied with 14,619 users (10,373 women [71%] and 3,919 men [26.8%]). Dietary products were promoted by 40% of the groups. Conclusions: Facebook is used as a means of communication and for sharing health information. Because many of these groups promote dietary products, their usefulness for health education is doubtful. Health organizations should participate in social media. Objetivo, conocer las características de los grupos de Facebook relacionados con la alimentación saludable. Método, estudio observacional transversal en Internet. Mediante el API de Facebook se seleccionaron los grupos abiertos en español relacionados con alimentación saludable. Variables estudiadas: nombre, descripción, categoría, número y sexo de los usuarios, fecha de creación, número de posts, contenido de los primeros 20 posts y última actualización. Resultados, se identificaron 281 grupos abiertos, pero se excluyeron 125 por no haber posts en el muro o no estar relacionados con alimentación saludable. Finalmente se incluyeron 156 grupos con 14.619 usuarios (10.373 mujeres [71%] y 3919 hombres [26,8%]). El 40% de los grupos promocionaban productos dietéticos. Conclusiones, Facebook se utiliza como medio de comunicación y para compartir información sobre alimentación. Se encontró un gran número de grupos que promocionan la venta de productos dietéticos, que hacen dudar de su utilidad para la educación en salud. Las entidades sanitarias deberían participar en las redes sociales.

This paper proposes a new methodology for assessing the efficiency of medical diagnostic systems and clinical decision support systems by using the feedback/opinions of medical experts. The methodology behind this work is based on a comparison between the expert feedback that has helped solve different clinical cases and the expert system that has evaluated these same cases. Once the results are returned, an arbitration process is carried out in order to ensure the correctness of the results provided by both methods. Once this process has been completed, the results are analyzed using Precision, Recall, Accuracy, Specificity and Matthews Correlation Coefficient (MCC) (PRAS-M) metrics. When the methodology is applied, the results obtained from a real diagnostic system allow researchers to establish the accuracy of the system based on objective facts. The methodology returns enough information to analyze the system's behavior for each disease in the knowledge base or across the entire knowledge base. It also returns data on the efficiency of the different assessors involved in the evaluation process, analyzing their behavior in the diagnostic process. The proposed work facilitates the evaluation of medical diagnostic systems, having a reliable process based on objective facts. The methodology presented in this research makes it possible to identify the main characteristics that define a medical diagnostic system and their values, allowing for system improvement. A good example of the results provided by the application of the methodology is shown in this paper. A diagnosis system was evaluated by means of this methodology, yielding positive results (statistically significant) when comparing the system with the assessors that participated in the evaluation process of the system through metrics such as recall (+27.54%) and MCC (+32.19%). These results demonstrate the real applicability of the methodology used.

To determine the features and use of groups related to healthy eating on Facebook.We carried out a cross-sectional study through the Internet. Using the API on Facebook, we included open groups related to healthy eating in the Spanish language. The variables studied were name, description, category, the number and gender of users, date of creation, number of posts, content of the first 20 posts, and the most recent update.We selected 281 open groups for inclusion in the study. Of these, 125 were excluded because the content was unrelated to healthy eating. Finally 156 groups were studied with 14,619 users (10,373 women [71%] and 3,919 men [26.8%]). Dietary products were promoted by 40% of the groups.Facebook is used as a means of communication and for sharing health information. Because many of these groups promote dietary products, their usefulness for health education is doubtful. Health organizations should participate in social media.

Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.