Programa de prevención del riesgo de suicidio «RENACE» / [“RENACE” suicide risk prevention program]

A pesar de que en España el suicidio supone la primera causa de muerte externa, no existe a nivel nacional un plan de prevención o intervención protocolizado. Este estudio tiene como objetivo final el diseño, la implementación y la evaluación de un nuevo programa de prevención del riesgo de suicidio en el Complejo Hospitalario Universitario de Albacete, llamado «RENACE». Para ello, se describe el perfil sociodemográfico y clínico de los pacientes con ideación y con conducta suicida. Se obtuvieron los siguientes resultados: la mayoría de pacientes fueron mujeres (59%) y el grupo etario más prevalente fue el de 31 a 65 años. Entre la población infantojuvenil, predominó el rango de edad de 14 a 17 años. En cuanto al perfil clínico, el diagnóstico principal fue trastornos relacionados con traumas y factores de estrés, seguido de trastornos depresivos. (AU)

Despite the fact that suicide is the leading cause of external death in Spain, there is no protocolized prevention or intervention plan at national level. The final aim of this study is the design, implementation and evaluation of a new suicide risk prevention program at the Albacete University Hospital Complex, called "RENACE". For this purpose, the sociodemographic and clinical profile of patients with suicidal ideation and behavior was described. The following results were obtained: the majority of patients were women (59%) and the most prevalent age group was 31 to 65 years old. Among the juvenile population, the predominant age range was 14 to 17 years. Regarding the clinical profile, the main diagnosis was trauma-related disorders and stress factors, followed by depressive disorders. (AU)

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.