Examining the relationship between COVID-19 and suicide in media coverage through Natural Language Processing analysis

Background and objectives Suicide is a major public health concern, media can influence its awareness, contagion, and prevention. In this study, we evaluated the relationship between the COVID-19 pandemic and suicide in media coverage through Natural Language Processing analysis (NPL). Methods To study how suicide is depicted in news media, Artificial Intelligence and Big Data techniques were used to analyze news and tweets, to extract or classify the topic to which they belonged. Results A granger causality analysis showed with significant p-value that an increase in covid news at the beginning of the pandemic explains a later rise in suicide-related news. An analysis based on correlation and structural causal models show a strong relationship between the appearance of subjects “health” and “covid”, and also between “covid” and “suicide”. Conclusions Our analysis also uncovers that the inclusion of suicide-related news in the category health has grown since the outbreak of the pandemic. The COVID-19 pandemic has posed an inflection point in the way suicide-related news are reported. Our study found that the increased media attention on suicide during the COVID-19 pandemic may indicate rising social awareness of suicide and mental health, which could lead to the development of new prevention tools. (AU)

Tactical optimisation and opponent analysis for football teams with big data mining and machine learning

Football tactical training is of great significance to the improvement of football level and the development of football, while the tactical training system is rich in content and diversified in structure, how to coordinate the content of tactical trainingsystem has become a key topic to improve the effectiveness of tactical training. In this paper, we design the method of tactical training for college football teams, aiming at building a tactical optimisation and opponent analysis model based on big data mining and machine learning from the reality of tactical training for college high-level football teams. Specifically, the convolutional neural network with two levels of cascade detection and regression in the model adopts the classic ideas of face key point detection and human body key point detection: the idea of cascade regression is used for the detection of the key point location from coarse to fine; the heat map of the key point obtained from the first level network is used as the supplemental information, and the original map is used for the feature fusion; the Heatmap, which has better effect, is used as the Ground Truth of the network; the Heatmap is used as the ground truth of the network; the Heatmap, which has better effect, is used as the ground truth of the network. The second stage regression network uses Heatmap as the Ground Truth of the network, which provides pixel-by-pixel supervision for the regression of the key points' positions and the prediction of whether the key pointsare visible ornot. In addition, this paper combines the idea of adversarial learning to design the loss function to solve the fuzzy problem of regression-to-the-mean when regressing Heatmap. The second-stage network is used as the generator, and the discriminator is designed to define the loss function to judge the reliability of the generated Heatmap (AU)

Fumadores y riesgo de muerte hospitalaria por COVID calculado con el procesamiento de lenguaje natural de SAVANA en el ámbito de Castilla-La Mancha / Smokers and risk of hospital death by COVID calculated with SAVANA's natural language processing in the Castilla-La Mancha area

Introducción Durante la pandemia de COVID se especuló que los pacientes con el virus que tenían relación con el tabaco podrían tener una menor probabilidad de agravamiento de la enfermedad o muerte. Para evaluar si existe una asociación entre el tabaquismo y el riesgo de mortalidad intrahospitalaria se utiliza la tecnología de Big Data y Procesamiento del Lenguaje Natural (PLN) de SAVANA. Método Se llevó a cabo un estudio de cohortes retrospectivo, observacional y sin intervención basado en datos de vida real extraídos de registros médicos de toda Castilla-La Mancha utilizando las técnicas de PLN e inteligencia artificial desarrolladas por SAVANA. El estudio abarcó toda la población de esta Comunidad con historia clínica electrónica en SESCAM que presentara diagnóstico de COVID desde el 1 de marzo de 2020 al 28 de febrero de 2021. Resultados Los fumadores tienen mayor porcentaje de factores de riesgo cardiovascular (hipertensión arterial, dislipemia y diabetes), EPOC, asma, EPID, CI, ECV, TEP, cáncer en general y cáncer de pulmón en particular, bronquiectasias, insuficiencia cardíaca y antecedentes de neumonía, de forma significativa (p<0,0001). Los pacientes exfumadores, fumadores y no fumadores tienen una diferencia de edad significativa. En cuanto a las muertes hospitalarias, fueron más frecuentes en el caso de los exfumadores, siguiendo los fumadores y luego los no fumadores (p<0,0001). Conclusión Existe un mayor riesgo de mortalidad intrahospitalaria en los pacientes infectados por SARS-CoV-2 que sean fumadores activos o hayan fumado en el pasado. (AU)

Introduction During the COVID pandemic, it was speculated that patients with the virus who were smoking-related might have a lower likelihood of disease exacerbation or death. To assess whether there is an association between smoking and risk of in-hospital mortality, SAVANA's big data and natural language processing (NLP) technology is used. Method A retrospective, observational, non-interventional cohort study was conducted based on real-life data extracted from medical records throughout Castilla-La Mancha using natural language processing and artificial intelligence techniques developed by SAVANA. The study covered the entire population of this region with Electronic Medical Records in SESCAM presenting with a diagnosis of COVID from March 1, 2020 to February 28, 2021. Results Smokers had a significantly higher percentage of cardiovascular risk factors (hypertension, dyslipidemia and diabetes), COPD, asthma, IDP, IC, CVD, PTE, cancer in general and lung cancer in particular, bronchiectasis, heart failure and a history of pneumonia (P<.0001). Former smokers, current smokers and non-smokers have a significant age difference. As for in-hospital deaths, they were more frequent in the case of ex-smokers, followed by smokers and then non-smokers (P<.0001). Conclusion There is an increased risk of dying in hospital in SARS-CoV-2-infected patients who are active smokers or have smoked in the past. (AU)

Fumadores y riesgo de muerte hospitalaria por COVID calculado con el procesamiento de lenguaje natural de SAVANA en el ámbito de Castilla-La Mancha / Smokers and risk of hospital death by COVID calculated with SAVANA's natural language processing in the Castilla-La Mancha area

Introducción Durante la pandemia de COVID se especuló que los pacientes con el virus que tenían relación con el tabaco podrían tener una menor probabilidad de agravamiento de la enfermedad o muerte. Para evaluar si existe una asociación entre el tabaquismo y el riesgo de mortalidad intrahospitalaria se utiliza la tecnología de Big Data y Procesamiento del Lenguaje Natural (PLN) de SAVANA. Método Se llevó a cabo un estudio de cohortes retrospectivo, observacional y sin intervención basado en datos de vida real extraídos de registros médicos de toda Castilla-La Mancha utilizando las técnicas de PLN e inteligencia artificial desarrolladas por SAVANA. El estudio abarcó toda la población de esta Comunidad con historia clínica electrónica en SESCAM que presentara diagnóstico de COVID desde el 1 de marzo de 2020 al 28 de febrero de 2021. Resultados Los fumadores tienen mayor porcentaje de factores de riesgo cardiovascular (hipertensión arterial, dislipemia y diabetes), EPOC, asma, EPID, CI, ECV, TEP, cáncer en general y cáncer de pulmón en particular, bronquiectasias, insuficiencia cardíaca y antecedentes de neumonía, de forma significativa (p<0,0001). Los pacientes exfumadores, fumadores y no fumadores tienen una diferencia de edad significativa. En cuanto a las muertes hospitalarias, fueron más frecuentes en el caso de los exfumadores, siguiendo los fumadores y luego los no fumadores (p<0,0001). Conclusión Existe un mayor riesgo de mortalidad intrahospitalaria en los pacientes infectados por SARS-CoV-2 que sean fumadores activos o hayan fumado en el pasado. (AU)

Introduction During the COVID pandemic, it was speculated that patients with the virus who were smoking-related might have a lower likelihood of disease exacerbation or death. To assess whether there is an association between smoking and risk of in-hospital mortality, SAVANA's big data and natural language processing (NLP) technology is used. Method A retrospective, observational, non-interventional cohort study was conducted based on real-life data extracted from medical records throughout Castilla-La Mancha using natural language processing and artificial intelligence techniques developed by SAVANA. The study covered the entire population of this region with Electronic Medical Records in SESCAM presenting with a diagnosis of COVID from March 1, 2020 to February 28, 2021. Results Smokers had a significantly higher percentage of cardiovascular risk factors (hypertension, dyslipidemia and diabetes), COPD, asthma, IDP, IC, CVD, PTE, cancer in general and lung cancer in particular, bronchiectasis, heart failure and a history of pneumonia (P<.0001). Former smokers, current smokers and non-smokers have a significant age difference. As for in-hospital deaths, they were more frequent in the case of ex-smokers, followed by smokers and then non-smokers (P<.0001). Conclusion There is an increased risk of dying in hospital in SARS-CoV-2-infected patients who are active smokers or have smoked in the past. (AU)

Text Mining and Visualisation of Post-Covid-19 Metaverse Online News Related to Sports in South Korea / Minería de textos y visualización del metaverso post-Covid-19 Noticias en línea relacionadas con deportes en Corea del Sur

The concept of the 'Metaverse' gained prominence following the COVID-19 pandemic, which coincided with the resurgence of the virtual world's economic ecosystem and advancements in IT technology. Recent studies have highlighted the significance of examining the establishment of societal perceptions and key social issues surrounding Metaverse in this rapidly advancing era. These studies have analysed popular content, the spread of sports awareness, and successful cases to shed light on this matter. The purpose of this study was to analyse online news data collected from the top three portal sites in South Korea using semantic network text mining technology. The study aimed to emphasise the significance of this data in promoting sports and physical activities. The study's findings indicate the need for collaboration among government ministries, departments, and agencies to facilitate the activation of metaverse-related startups, small and medium-sized enterprises, universities, and research institutes. Additionally, metaverse companies should prioritise the creation of high-quality content and recognise the significance of sports beyond online gaming. This should be accompanied by advancements in 5G-based hardware and software. This study aims to serve as a valuable reference for understanding and preparing for the upcoming metaverse era. It explores strategies for ensuring the sustainable growth of the metaverse across various aspects of life, including sports awareness and prevalence.(AU)

Unveiling Metaverse Social Trends: Analysing Big Data Regarding Online Sports News With LDA-Based Topic Modelling / Revelación de tendencias sociales del metaverso: análisis de big data sobre noticias deportivas en línea con modelado de temas basado en LDA

The rise of digitalization has led to a significant increase in the attention given to online sports news. In a similar vein, the Metaverse has gone through various stages of evaluation. At present, it is in a developmental phase where real-world socio-economic and cultural activities are seamlessly integrated into a 3D virtual environment. Understanding the impact and implications of the Metaverse 3.0 era is highly sought after. This study seeks to investigate social issues related to the Metaverse in South Korea. It will analyse a large dataset of online sports news articles from June 2021 to May 2022 using LDA-based topic modelling. As a result, four main topics were identified: a new platform, a new business model, pervasive online cultural and educational media, and a new frontier for business and investment. New Platform (Topic 1) showcased keywords indicating the potential of Metaverse as a cutting-edge platform, while New Business (Topic 2) highlighted keywords indicating digital media companies' pursuit of a new profit model within the virtual realm of Metaverse. The increasing prevalence of online culture and educational media (Topic 3) highlights the growing demand for a wide range of content that caters to the virtual world's cultural and educational experiences. "New Frontier for Business and Investment (Topic 4) highlights the significant involvement of digital media companies in acquiring investments, technologies, and intellectual property (IP) to gain a competitive edge in the global Metaverse market. This study solely examined online sports data, but future research could encompass a wider range of raw data sources, such as interviews with practitioners, user reviews, and research reports. By doing so, a more comprehensive understanding of the potential challenges that the Metaverse may encounter can be obtained.(AU)

Examining the Role of Big Data and Block Chain Technology in Advancing High-Quality Development within the Sports Industry's Digital Economy / Examinando el papel de Big Data y la tecnología Block Chain en el avance del desarrollo de alta calidad dentro de la economía digital de la industria del deporte

In the ever-evolving landscape of the global economy, big data, the new generation of artificial intelligence, and block chain technology have risen as pivotal drivers of the data economy. These technologies are playing an increasingly vital role in catalyzing transformative changes within the sports industry and the broader digital economy. As the world economic patterns continue to shift and Internet technologies advance, the digital economy, particularly within the sports sector, faces higher demands and opportunities for innovation. In the context of China's economic development, the digital economy has emerged as a primary pathway to achieving high-quality economic growth. This paper embarks on a theoretical exploration of the profound impact of big data and blockchain technology within the sports industry, examining their current applications and potential. The research presented here seeks to unravel the intricate mechanisms through which big data and blockchain technology drive high-quality development within the sports sector's digital economy. By shedding light on these dynamics, this paper contributes valuable insights to guide and optimize the future of China's economic development, with a strong emphasis on the sports industry as a critical focal point for innovation and growth.(AU)

Understanding Severe Asthma Through Small and Big Data in Spanish Hospitals: The PAGE Study

Background: Data on the prevalence of severe asthma (SA) are limited. Electronic health records (EHRs) offer a unique research opportunity to test machine learning (ML) tools in epidemiological studies. Our aim was to estimate the prevalence of SA among asthma patients seen in hospital asthma units, using both ML-based and traditional research methodologies. Our secondary objective was to describe patients with nonsevere asthma (NSA) and SA over a follow-up of 12 months. Methods: PAGE is a multicenter, controlled, observational study conducted in 36 Spanish hospitals and split into 2 phases: a cross-sectional phase for estimation of the prevalence of SA and a prospective phase (3 visits in 12 months) for the follow-up and characterization of SA and NSA patients. A substudy with ML was performed in 6 hospitals. Our ML tool uses EHRead technology, which extracts clinical concepts from EHRs and standardizes them to SNOMED CT. Results: The prevalence of SA among asthma patients in Spanish hospitals was 20.1%, compared with 9.7% using the ML tool. The proportion of SA phenotypes and the features of patients followed up were consistent with previous studies. The clinical predictions of patients’ clinical course were unreliable, and ML found only 2 predictive models with discriminatory power to predict outcomes. Conclusion: This study is the first to estimate the prevalence of SA in hospitalized asthma patients and to predict patient outcomes using both standard and ML-based research techniques. Our findings offer relevant insights for further epidemiological and clinical research in SA (AU)

Antecedentes: Los datos sobre la prevalencia del asma grave (SA) son limitados. La implantación de las historias clínicas electrónicas (EHR) ofrece una oportunidad única de investigación con tecnologías de aprendizaje máquina (ML) en los estudios epidemiológicos. El objetivo fue estimar la prevalencia del SA entre los pacientes atendidos en las unidades de asma hospitalarias, utilizando el ML como la metodología de investigación tradicional. Los objetivos secundarios fueron describir los pacientes con asma no grave (NSA) y con SA durante un período de seguimiento de 12 meses. Métodos: El estudio PAGE es un estudio multicéntrico, controlado y observacional realizado en 36 hospitales españoles y dividido en dos fases: una primera fase transversal para la estimación de la prevalencia de AS, y una segunda fase prospectiva (3 visitas en 12 meses) para el seguimiento y caracterización de los pacientes con SA y NSA. Se incluyó un subestudio con ML en 6 hospitales. Resultados: Se obtuvo una prevalencia de SA del 20,1% entre los pacientes asmáticos, frente al 9,7% de la herramienta ML. La proporción de fenotipos de SA y las características de los pacientes en seguimiento fueron consistentes con estudios anteriores. Las predicciones clínicas de la evolución de los pacientes fueron poco fiables, mientras que el ML sólo encontró dos modelos predictivos con potencial discriminatorio para predecir resultados. Conclusión: Este estudio es el primero en estimar la prevalencia del SA, en una población hospitalaria de pacientes con asma, y en predecir los resultados de los pacientes utilizando técnicas estándar y de ML (AU)

Human Resources Analytics: A systematic Review from a Sustainable Management Approach / La analítica de recursos humanos: una revisión sistemática desde la perspectiva de una gestión sostenible

Human Resources Analytics (HRA) is drawing more attention every year, and will be crucial to human resource development. However, the literature around the topic would appear to be more promotional than descriptive. With this in mind, we conducted a systematic literature review and content analysis with the following objectives: first, to address the current state of HRA and second, to propose a framework for the development of HRA as a sustainable practice. We analyzed 79 articles from research databases and found 34 empirical studies for subsequent content analysis. While the main results reflect the relative newness of the field of HRA, with the majority of the empirical articles focusing on financial aspects, they also reveal the growing importance given to ethics. Finally, we propose a framework for the development of sustainable HRA based on the triple bottom line and discuss the implications of our findings for researchers and practitioners.(AU)

La analítica de recursos humanos (ARH) atrae cada vez más atención en los últimos años y será crucial para el desarrollo del ámbito de los recursos humanos. No obstante, la literatura sobre el tema parece ser más promocional que descriptiva. Para comprobar esto, llevamos a cabo una revisión sistemática de la literatura y un análisis de contenido con los siguientes objetivos: primero, abordar el estado actual la ARH y segundo, proponer un marco para el desarrollo de la AHR como una práctica sostenible. Analizamos 79 artículos de investigación incluidos en las más prestigiosas bases de datos y encontramos 34 estudios empíricos para su posterior análisis de contenido. Los principales resultados reflejan la relativa novedad del campo de la ARH, estando centrados la mayoría de los artículos en los aspectos financieros. No obstante, también se observa la creciente importancia dada a la ética. Finalmente, proponemos un marco para el desarrollo de una ARH basada en la triple cuenta de resultados (económica, social y medioambiental, y se discuten las implicaciones prácticas y teóricas de nuestros hallazgos.(AU)

Dataísmo (de la salud), tecnología y privacidad / (Health) dataism, technology and privacy

El dataísmo puede privar al individuo de su privacidad. Las personas reflexionan sobre el coste de oportunidad que supone ceder sus datos y otorgan mayor importancia a la efectividad en la lucha contra enfermedades y pandemias frente a su uso ilícito, ilegal o poco ético. El big data es un bien común de la humanidad, y compartir datos puede salvar vidas, pero aprovechémoslo aplicando correctamente la ética de los datos, donde los gobiernos y organizaciones estén implicados y se respete el derecho fundamental de protección de datos. (AU)

Dataism can deprive the individuals of their privacy. People are reflecting on the opportunity cost of giving away their data and are placing greater importance on the effectiveness of fighting diseases and pandemics than on its illicit, illegal or unethical use. Big data is a common good of humanity, and sharing data can save lives, but let’s harness it with the right application of data ethics, where governments and organisations are involved and the fundamental right to data protection is respected. (AU)

La era del big data: análisis del lenguaje natural mediante la aplicación de folksonomía / The big data era: The usefulness of folksonomy for natural language processing

Antecedentes y objetivo: Gran parte de la información médica que se deriva de la práctica clínica habitual queda recogida en forma de lenguaje natural en los informes médicos. Clásicamente, la extracción de información clínica para su posterior análisis a partir de los informes médicos requiere de la lectura y revisión manual de cada uno de ellos con la consiguiente inversión de tiempo. El objetivo de este proyecto piloto ha sido evaluar la utilidad de la folksonomía para la extracción y análisis rápido de los datos que contienen los informes médicos. Material y métodos: En este proyecto piloto hemos utilizado la folksonomía para el análisis y la rápida extracción de datos de 1.631 informes médicos de alta de hospitalización del Servicio de Nefrología del Hospital del Mar sin necesidad de crear una base de datos estructurada previamente. Resultados: A partir de determinadas preguntas sobre la práctica médica habitual (tratamiento hipoglicemiante de los pacientes diabéticos, tratamiento antihipertensivo y manejo de los inhibidores del sistema renina angiotensina durante el ingreso en nefrología y análisis de datos relacionados con la esfera emocional de los pacientes renales) la herramienta ha permitido estructurar y analizar la información contenida en texto libre en los informes de alta. Conclusiones: La aplicación de folksonomía a los informes médicos nos permite transformar la información contenida en lenguaje natural en una serie de datos estructurados y analizables de manera automática sin necesidad de proceder a la revisión manual de los mismos. (AU)

Background: A huge amount of clinical data is daily generated and it is usually filed in clinical reports as natural language. Data extraction and further analysis requires reading and manual review of each report, which is a time consuming process. With the aim to test folksonomy to quickly obtain and analyze the information contained in medial reports we set up this study. Methods and objectives:We have used folksonomy to quickly obtain and analyse data from 1631 discharge clinical reports from Nephrology Department of Hospital del Mar, without the need to create an structured database. Results: After posing some questions related to daily clinical practice (hypoglycaemic drugs used in diabetic patients, antihypertensive drugs and the use of renin angiotensin blockers during hospitalisation in the nephrology department and data related to emotional environment of patients with chronic kidney disease) this tool has allowed the conversion of unstructured information in natural language into a structured pool of data for its further analysis. Conclusions: Folksonomy allows the conversion of the information contained in clinical reports as natural language into a pool of structured data which can be further easily analysed without the need of the classical manual review of the reports. (AU)

Big Data Analysis on Spatial Distribution Features of College Students' Physical Literacy / Análisis de Big Data sobre características de distribución espacial de la alfabetización física de estudiantes universitarios

To effectively improve college students' physical literacy, it is required to conduct a thorough examination of the geographical distribution characteristics of their physical literacy. Only a few domestic researchers have examined the system for measuring and evaluating physical literacy, and they have been unable to develop a cohesive study framework. Additionally, little research has been conducted on the spatial distribution characteristics of college students' physical literacy. As a result, this article performs a large-scale analysis of the spatial distribution characteristics of college students' physical literacy. To begin, certain established evaluation indices for college students' physical literacy were enhanced using spatiotemporal data on college students' physical literacy, and an evaluation index system (EIS) for college students' physical literacy was built. Following that, the technique for ranking preferences according to their similarity to the ideal solution (TOPSIS) was used to compare college students' physical literacy, and the assessment flow was described. The analytic hierarchy process (AHP) was used with the entropy value approach to optimise the index weights. Following that, the authors discussed the process of spatial distribution analysis and its application to college students' physical literacy. The standard deviational ellipse was used to determine the spatial distribution direction of college students' physical literacy. The average nearest neighbour was used to assess the degree of concentration or dispersion of spatial points, and the kernel density tool was used to analyse both the global features of the distribution space of college students' physical literacy and the structural elements of the distribution space of each. Finally, distributions of physical literacy among college students in various locations were reported, demonstrating the scientific quality of our analysis approach.(AU)

El valor de los datos y su aplicabilidad en el Sector Sanitario / The value of data and its applicability in the Health Sector

Actualmente las noticias y/o artículos sobre la utilización de la Inteligencia Artificial (IA) y el Big Data nos están inundando y esta situación se ha agudizado con la pandemia, donde se ha dado una gran importancia a su utilización y las diversas aplicaciones en todos los sectores. Unos ámbitos tecnológicos y de oportunidades que cada vez se encuentran más presentes en nuestro día a día. El sector que más crecimiento ha experimento durante este tiempo de pandemia es, sin lugar a dudas, el sector sanitario. La imperiosa necesidad ha fomentado y agilizado el uso de estas tecnologías. La utilización de datos para poder acometer tratamientos en un breve tiempo, ver las evoluciones de las diferentes enfermedades y predecir su estado es lo que ha impulsado su utilización y donde debido a la situación cualquier ayuda era y es poca. Desde este artículo pretendemos dar una explicación de los beneficios del uso de la IA y las diferentes técnicas del Big Data, tanto en el estudio y evolución de enfermedades como en su prevención, detección, seguimiento y tratamiento (AU)

Currently news and/or articles on the use of Artificial Intelligence and the Big Data are flooding us and this situation has worsened with the pandemic, where great importance has been given to its use and the various applications in all sectors. Some areas of technology and opportunities that are increasingly are more present in our day to day. The sector that has experienced the most growth during this time of pandemic is, without a doubt, the Health sector. The imperative need has fostered and expedited the use of these technologies. The use of data to be able to undertake treatments in a short time, see the evolutions of the different diseases and predict their state is what has driven its use and where due to the situation any help was and is little. From this article we intend to give an explanation of the benefits of using the Artificial Intelligence and the different Big Data techniques, both in the study and evolution of diseases as in their prevention, detection, monitoring and treatment (AU)

Inteligencia artificial en dermatología: ¿amenaza u oportunidad? / Artificial Intelligence in Dermatology: A Threat or an Opportunity?

La irrupción de la inteligencia artificial (IA) a nivel mundial ha supuesto un antes y un después en nuestras vidas, generando grandes mejoras en diferentes sectores, como el de la automoción y el agroalimentario, entre otros, lo que ha llevado a denominarla la cuarta revolución industrial. La AI, capaz de aprender de forma automatizada y de ayudar al profesional a mejorar sus procesos, promete cambiar el ámbito sanitario tal y como lo conocemos mediante: 1) aplicaciones capaces de generar salud en la población general a partir del uso de información de calidad y de segmentación de consejos basados en modelos de predicción; 2) modelos capaces de generar algoritmos de predicción a partir de datos anonimizados procedentes de información clínica, a fin de mejorar la prevención primaria; 3) sistemas de análisis de imagen capaces de dar a los profesionales de la salud un soporte extra en la toma de decisiones, mejorando la prevención secundaria; y 4) aplicación de robótica combinada en la mejora de procesos ligados al ámbito de salud y bienestar. Sin embargo, la falta de conocimiento tanto en este tipo de tecnología, como en los términos y la metodología de validación de la misma, hace que la clase médica dude en si esta revolución supone una amenaza o una oportunidad para la profesión. En el presente artículo de revisión pretendemos introducir una serie de aspectos básicos de la IA aplicada a la dermatología, así como los principales avances sucedidos en este campo en los últimos 5 años (AU)

The worldwide explosion of interest in artificial intelligence (AI) has created a before-and-after moment in our lives by generating great improvements in such sectors as the automotive and food production industries. AI has even been called the fourth industrial revolution. Machine learning through AI is helping to improve professional processes and promises to transform the health care sector as we know it in various ways: 1) through applications able to promote health in the general population by providing high-quality information and offering advice for different segments of the population based on prediction models; 2) by developing prediction models based on anonymized clinical data, for preventive purposes in primary care; 3) by analyzing images to provide additional decision-making support for health care providers, for improving specialist care at the secondary level; and 4) through robotics applied to processes that promote health and well-being. However, the medical profession harbors doubts about whether this revolution is a threat or an opportunity owing to a lack of understanding of AI technology and the methods used to validate its applications. This article outlines basic aspects of AI as it is applied in dermatology and reviews the main advances achieved in the last 5 years (AU)

[Translated article] Artificial intelligence in dermatology: A threat or an opportunity? / Inteligencia artificial en dermatología: ¿amenaza u oportunidad?

The worldwide explosion of interest in artificial intelligence (AI) has created a before-and-after moment in our lives by generating great improvements in such sectors as the automotive and food production industries. AI has even been called the fourth industrial revolution. Machine learning through AI is helping to improve professional processes and promises to transform the health care sector as we know it in various ways: 1) through applications able to promote health in the general population by providing high-quality information and offering advice for different segments of the population based on prediction models; 2) by developing prediction models based on anonymized clinical data, for preventive purposes in primary care; 3) by analyzing images to provide additional decision-making support for health care providers, for improving specialist care at the secondary level; and 4) through robotics applied to processes that promote health and well-being. However, the medical profession harbors doubts about whether this revolution is a threat or an opportunity owing to a lack of understanding of AI technology and the methods used to validate its applications. This article outlines basic aspects of AI as it is applied in dermatology and reviews the main advances achieved in the last 5 years (AU)

La irrupción de la inteligencia artificial (IA) a nivel mundial ha supuesto un antes y un después en nuestras vidas, generando grandes mejoras en diferentes sectores, como el de la automoción y el agroalimentario, entre otros, lo que ha llevado a denominarla la cuarta revolución industrial. La AI, capaz de aprender de forma automatizada y de ayudar al profesional a mejorar sus procesos, promete cambiar el ámbito sanitario tal y como lo conocemos mediante: 1) aplicaciones capaces de generar salud en la población general a partir del uso de información de calidad y de segmentación de consejos basados en modelos de predicción; 2) modelos capaces de generar algoritmos de predicción a partir de datos anonimizados procedentes de información clínica, a fin de mejorar la prevención primaria; 3) sistemas de análisis de imagen capaces de dar a los profesionales de la salud un soporte extra en la toma de decisiones, mejorando la prevención secundaria; y 4) aplicación de robótica combinada en la mejora de procesos ligados al ámbito de salud y bienestar. Sin embargo, la falta de conocimiento tanto en este tipo de tecnología, como en los términos y la metodología de validación de la misma, hace que la clase médica dude en si esta revolución supone una amenaza o una oportunidad para la profesión. En el presente artículo de revisión pretendemos introducir una serie de aspectos básicos de la IA aplicada a la dermatología, así como los principales avances sucedidos en este campo en los últimos 5 años (AU)

Psychological Crisis Intervention of College Sports Majors Based on Big Data Analysis / Intervención en Crisis Psicológicas de Mayores del Deporte Universitario Basada en Análisis de Big Data

In recent years, the psychological problems of college students have attracted extensive attention. It is of great practical significance to timely and arcuately intervene in college sports majors faced with psychological crisis (PC). However, the existing studies mainly analyze the mood and psychological state at a certain moment, but rarely track the psychological health state of different types of college students. This paper proposes a way to intervene and predict the PC of college sports majors based on big data analysis. Firstly, the massive evaluation data were collected from a psychological census database on PC of college sports majors and subjected to data mining. Besides, a PC evaluation model was established based on the decision tree (DT) algorithm. Next, the behavior big data of college sports majors in social network were fully utilized, and a PC intervention and prediction method was developed based on social network readme texts. Further, the authors extracted features from readme texts, evaluated the level of PC risk, and analyzed the longitudinal features. Finally, the proposed model was proved valid through experiments. This paper effectively applies new technologies to the data mining of the typical behaviors of college sports majors, and thereby realizes accurate PC warning. The research results are of great significance to improving the psychological health of college students.(AU)