Pediatrics in Artificial Intelligence Era: A Systematic Review on Challenges, Opportunities, and Explainability

Background: The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, the practical application of AI still faces a number of key challenges. Consequently, there is a requirement for a concise overview of the roles of AI across the multiple domains of pediatric medicine, which the current study seeks to address. Aim: To systematically assess the challenges, opportunities, and explainability of AI in pediatric medicine. Methodology: A systematic search was carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, and grey literature using search terms related to machine learning (ML) and AI for the years 2016 to 2022 in the English language. A total of 210 articles were retrieved that were screened with PRISMA for abstract, year, language, context, and proximal relevance to research aims. A thematic analysis was carried out to extract findings from the included studies. Results: Twenty articles were selected for data abstraction and analysis, with three consistent themes emerging from these articles. In particular, eleven articles address the current state-of-the-art application of AI in diagnosing and predicting health conditions such as behavioral and mental health, cancer, syndromic and metabolic diseases. Five articles highlight the specific challenges of AI deployment in pediatric medicines: data security, handling, authentication, and validation. Four articles set out future opportunities for AI to be adapted: the incorporation of Big Data, cloud computing, precision medicine, and clinical decision support systems. These studies collectively critically evaluate the potential of AI in overcoming current barriers to adoption. Conclusion: AI is proving disruptive within pediatric medicine and is presently associated with challenges, opportunities, and the need for explainability. AI should be viewed as a tool to enhance and support clinical decision-making rather than a substitute for human judgement and expertise. Future research should consequently focus on obtaining comprehensive data to ensure the generalizability of research findings.

Automated extraction of information from free text of Spanish oncology pathology reports

Background: Pathology reports are stored as unstructured, ungrammatical, fragmented, and abbreviated free text with linguistic variability among pathologists. For this reason, tumor information extraction requires a significant human effort. Recording data in an efficient and high-quality format is essential in implementing and establishing a hospital-based-cancer registry Objective: This study aimed to describe implementing a natural language processing algorithm for oncology pathology reports. Methods: An algorithm was developed to process oncology pathology reports in Spanish to extract 20 medical descriptors. The approach is based on the successive coincidence of regular expressions. Results: The validation was performed with 140 pathological reports. The topography identification was performed manually by humans and the algorithm in all reports. The human identified morphology in 138 reports and by the algorithm in 137. The average fuzzy matching score was 68.3 for Topography and 89.5 for Morphology. Conclusions: A preliminary algorithm validation against human extraction was performed over a small set of reports with satisfactory results. This shows that a regular-expression approach can accurately and precisely extract multiple specimen attributes from free-text Spanish pathology reports. Additionally, we developed a website to facilitate collaborative validation at a larger scale which may be helpful for future research on the subject.

Introducción: Los reportes de patología están almacenados como texto libre sin estructura, gramática, fragmentados o abreviados, con variabilidad lingüística entre patólogos. Por esta razón, la extracción de información de tumores requiere un esfuerzo humano significativo. Almacenar información en un formato eficiente y de alta calidad es esencial para implementar y establecer un registro hospitalario de cáncer. Objetivo: Este estudio busca describir la implementación de un algoritmo de Procesamiento de Lenguaje Natural para reportes de patología oncológica. Métodos: Desarrollamos un algoritmo para procesar reportes de patología oncológica en Español, con el objetivo de extraer 20 descriptores médicos. El abordaje se basa en la coincidencia sucesiva de expresiones regulares. Resultados: La validación se hizo con 140 reportes de patología. La identificación topográfica se realizó por humanos y por el algoritmo en todos los reportes. La morfología fue identificada por humanos en 138 reportes y por el algoritmo en 137. El valor de coincidencias parciales (fuzzy matches) promedio fue de 68.3 para Topografía y 89.5 para Morfología. Conclusiones: Se hizo una validación preliminar del algoritmo contra extracción humana sobre un pequeño grupo de reportes, con resultados satisfactorios. Esto muestra que múltiples atributos del espécimen pueden ser extraídos de manera precisa de texto libre de reportes de patología en Español, usando un abordaje de expresiones regulares. Adicionalmente, desarrollamos una página web para facilitar la validación colaborativa a gran escala, lo que puede ser beneficioso para futuras investigaciones en el tema.

O futuro das avaliações por meio das redes sociais / The future of assessments through social media / El futuro de las evaluaciones mediante las redes sociales

O objetivo geral da presente pesquisa é discutir as principais inovações em avaliação psicológica possibilitadas pela existência das redes sociais. Mais especificamente, iremos caracterizar as práticas (e fragilidades) da avaliação psicológica convencionais, os métodos mais modernos de psicometria computacional e exemplos de aplicações de psicometria computacional a partir de dados provenientes de redes sociais. De forma geral, as práticas convencionais da avaliação psicológica da psicometria foram criadas no século XIX e muitas de suas práticas, desenvolvidas próximo à metade do século XX, continuam sendo utilizadas de forma pouco crítica. Como alternativa, a psicometria computacional, uma abordagem da psicometria que combina métodos da ciência da computação orientados a dados e teoria psicométrica, tem sido utilizada para gerar inovações na área de avaliação. Por fim, discutimos algumas aplicações da psicometria computacional e como essas inovações irão, provavelmente, gerar mudanças profundas no contexto de avaliação.(AU)

The overall aim of this study was to discuss the main innovations in psychological assessment made possible by the existence of social networks. More specifically, we characterize the practices (and weaknesses) of conventional psychological assessment, the most modern methods of computational psychometry and examples of applications of computational psychometry using data from social networks. In general, the conventional practices of psychological assessment of psychometrics were created in the 19th century and many of its practices, developed around the middle of the 20th century, continue to be used in a non-critical way. As an alternative, computational psychometrics, an approach to psychometrics that combines data-driven methods of computer science and psychometric theory, has been used to generate innovations in the area of assessment. Finally, we discuss some applications of computational psychometrics and how these innovations are likely to generate profound changes in the assessment context.(AU)

El objetivo general de esta investigación es discutir las principales innovaciones, en materia de evaluación psicológica, posibilitadas por la existencia de las redes sociales. Más específicamente, caracterizaremos las prácticas convencionales (y debilidades) de la evaluación psicológica, los métodos más modernos de psicometría computacional y ejemplos de aplicaciones de psicometría computacional a partir de los datos de redes sociales. En general, las prácticas convencionales de evaluación psicológica de la psicometría se crearon en el siglo XIX y muchas de sus prácticas, desarrolladas a mediados del siglo XX, continúan utilizándose de forma acrítica. Como alternativa, la psicometría computacional, un enfoque de la psicometría que combina métodos informáticos basados en datos y teoría psicométrica, se ha empleado para generar innovaciones en el área de la evaluación. Por último, discutimos algunas aplicaciones de la psicometría computacional y cómo es probable que estas innovaciones generen cambios profundos en el contexto de la evaluación.(AU)

A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la / The scientific research in the age of Big data: five ways that show how the Big data harms the science, and how we can save it / La investigación científica en la era del Big data: cinco maneras que muestran como el Big data perjudica la ciencia, y como la salvar

O livro A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la, de Sabina Leonelli, publicado pela Editora Fiocruz em 2022, explora em seus capítulos as definições do termo Big data e os seus impactos negativos na pesquisa científica. Em seguida, a autora revela uma nova abordagem epistemológica para o Big data e, por fim, apresenta um conjunto de propostas para a pesquisa científica. A revisão e atualização de definições, tanto quanto as importantes reflexões e os questionamentos por um uso consciente do Big data na pesquisa científica fazem com que a obra adicione importantes contribuições à biblioteca do pesquisador de informação e comunicação em saúde

The book titled A pesquisa científica na era do Big Data: cinco maneiras que mostram como o Big Data prejudica a ciência, e como podemos salvá-la [The scientific research in the age of Big Data: five ways that show how the Big Data harms the science, and how we can save it], by Sabina Leonelli, published in 2002, by Editora Fiocruz, explores in its chapters the definitions of Big Data and its negative impacts on scientific research. Then, the author reveals a new epistemological approach to Big data and finally she presents a set of proposals for developing a good scientific research. The literature review and updating of definitions as well as the important reflections and questions for a conscious use of Big data in scientific research make the work an important contribution to the researcher's library of the information and communication about health.

El libro denominado A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la [La investigación científica en la era del Big data: cinco maneras que muestran como el Big data perjudica la ciencia, y como la salvar], de Sabina Leonelli, publicado en 2002, por la Editora Fiocruz, explora em sus capítulos las definiciones de Big data y sus impactos negativos en la investigación científica. A continuación, la autora revela un nuevo enfoque epistemológico del Big data y, al fin y al cabo, presenta un conjunto de propuestas para desarrollar una investigación científica de cualidad. La revisión de literatura y la actualización de las definiciones, así como las importantes reflexiones y discusiones para un uso consciente del Big data en la investigación científica, hacen de la obra un aporte importante a la biblioteca del investigador de la información y la comunicación acerca de la salud

Como estávamos antes? Uma análise da oferta potencial e desigualdade de acesso geográfico aos recursos críticos para tratamento de COVID-19 / How were we before? An analysis of the potential supply and inequality in the geographic access to critical resources for the COVID-19 treatment

Resumo O objetivo deste artigo é analisar a situação da Área Metropolitana de Brasília (AMB) antes do início da pandemia de COVID-19 com foco na disponibilidade e acessibilidade de recursos críticos para o tratamento da crise aguda respiratória causada pelo vírus SARS-CoV-2. Mapeamento geográfico da população e geolocalização dos estabelecimentos e recursos de saúde, construção de rede de relacionamentos entre a demanda potencial ao sistema de saúde público e a oferta de recursos existente em dez/2019. Análise baseada na teoria de redes complexas cruzando dados socioeconômicos disponíveis no CENSO, dados do Cadastro Nacional de Estabelecimentos de Saúde (CNES) e o micro relacionamento dos setores censitários e suas populações com o estoque e disponibilidade de recursos do tipo Leito de UTI Adulto Tipo II/III e Respiradores Mecânicos. Estabelecimentos do Distrito Federal (DF) concentram mais de 75% dos relacionamentos de acesso potencial aos recursos críticos para o tratamento de COVID-19. Embora as regiões do entorno do DF, pertencentes ao Goiás, apresentem a maior vulnerabilidade relativa no território estudado, são também as mais carentes de acessibilidade e disponibilidade de recursos, evidenciando um desequilíbrio assistencial dentro da região da AMB.

Abstract The objective was to analyze the situation of the Metropolitan Area of Brasília (AMB) before the onset of the COVID-19 pandemic, focusing on the availability and geographical accessibility of critical resources for the treatment of acute respiratory crises caused by the SARS-CoV-2 virus. Geographic mapping of the population within the territory and geolocation of health facilities and resources, construction of a relationship network between the potential demand simulated to the public health system and the supply of resources available in December 2019. The relationship analysis is based on the theory of complex networks crossing socioeconomic data available in the CENSUS and information from the National Registry of Health Establishments (CNES) and analyzing the micro relationship of census tracts with the stock and availability of health resources concerning Adult ICU Bed Type II/III and Respirators/Ventilators. The Federal District (DF) health facilities concentrate more than 75% of the relationships of potential access to critical resources for the treatment of COVID-19. Although the regions surrounding the DF, belonging to Goiás state, have the greatest relative vulnerability in the studied territory, they are also the most lacking in spatial accessibility and availability of resources, evidencing a care imbalance within the AMB region.

E se o scanCOVID-19 tivesse nascido em 2022? / And if the scanCOVID-19 had been born in 2002? / Y si el scanCOVID-19 hubiese nacido en 2022?

Acompanhar a velocidade das mudanças, qualitativas e quantitativas, na produção de conhecimento em tempos de pandemia de covid-19 impulsionou o desenvolvimento de inúmeras iniciativas de monitoramento da informação científica. O scanCOVID-19 foi uma delas. Essa nota de conjuntura procura situar a importância do investimento em projetos dessa natureza, que possam ter reflexos nas relações entre ciência, Estado e sociedade.

Keeping up with the speed of qualitative and quantitative changes in the production of knowledge in times of the covid-19 pandemic was a stimulus to the development of numerous initiatives to track scientific information. The scanCOVID-19 was one of them. This note seeks to situate the importance of investing in projects of the same nature, which may have effects on the relations between science, state, and society.

Acompañar la velocidad de los cambios, cualitativos y cuantitativos, en la producción de conocimiento en tiempos de la pandemia de covid-19 ha impulsado el desarrollo de numerosas iniciativas de seguimiento de la información científica. El scanCOVID-19 fue uno de ellos. Esta nota de coyuntura busca situar la importancia de invertir en proyectos de esta naturaleza, que puedan tener efectos sobre las relaciones entre ciencia, Estado y sociedad.

Plataforma de analítica de datos para la caracterización poblacional y la evaluación del riesgo cardiovascular en pacientes del centro-occidente de Colombia / Data analytics platform for population characterization and cardiovascular risk assessment in patients from the west-center of Colombia

Resumen Introducción: Las enfermedades cardiovasculares son la primera causa de muerte en el mundo. Por tanto, muchas investigaciones han sido dirigidas hacia la predicción del riesgo cardiovascular, con el fin de poder evitarlo. Asimismo, se ha buscado la implementación de sistemas que involucren el análisis de datos automatizados que permita que la información se ponga a disposición, no solo del personal administrativo y directivo, sino también del personal clínico, para mejorar el control de las patologías. Objetivo: Construir una herramienta para la caracterización poblacional y la evaluación del riesgo cardiovascular en pacientes del centro-occidente de Colombia. Materiales y métodos: Se propone la construcción de una plataforma de análisis de datos sociodemográficos y clínicos. El modelo general de diseño de la plataforma es el desarrollo evolutivo, que entrelaza actividades de especificación, desarrollo y validación. La plataforma presenta un modelo vista-controlador que permite la creación de plantillas dinámicas distribuidas en módulos de acceso controlados por perfiles de usuario. Resultados: Se implementó el cálculo automatizado del riesgo de enfermedad cardiovascular y la emisión de alertas tempranas, lo cual mejoró la gestión de los procesos clínicos, así como el apoyo a la toma de decisiones administrativas, a través de la conformación de dos módulos interactivos en la plataforma. Conclusiones: La unión de saberes clínicos, administrativos y de ingeniería permitió la consolidación de una herramienta que contribuye en el monitoreo y trazabilidad de los pacientes, orientando la priorización de posibles intervenciones que impacten en la salud de estos.

Abstract Introduction: Cardiovascular diseases are the leading cause of death in the world. Countless research has been directed towards the prediction of cardiovascular risk, in order to avoid the threat. Furthermore, the implementation automated data analysis tools have been sought to allow for information to be made readily available, not only to administrative and managerial staff, but also to clinical staff to improve the control of pathologies. Objective: To build a tool for the characterization of the population and the evaluation of cardiovascular risk in patients from central-western Colombia. Materials and methods: The construction of a platform for the analysis of sociodemographic and clinical data is proposed. The overall platform design model is evolutionary development, which intertwines specification, development, and validation activities. The platform presents a Vista-Controller model, which allows the creation of dynamic templates distributed in access modules controlled by user profiles. Results: The automated calculation of cardiovascular disease risk and the issuance of early warnings were implemented, which improved the management of clinical processes, as well as support for administrative decision-making, through the creation of two interactive modules on the platform. Conclusions: The union of clinical, administrative and engineering knowledge allowed the consolidation of a tool that contributes to the monitoring and traceability of patients, which guides the prioritization of possible interventions that impact the health of patients.

La informetría y el análisis del discurso aplicados a la producción científica en la Ciencia de Datos y Ciencia de la información / Informetrics and discourse analysis applied to scientific production in Data Science and Information Science

Resumen Objetivo. Investigar la producción científica en la intersección temática de la Ciencia de Datos (CD) y la Ciencia de la Información (CI). Método. Estudio informétrico, descriptivo, y de primera incursión, en el análisis del discurso escrito de los textos académicos incluidos en la Web of Science (WoS), periodo 1900 al 6 de noviembre de 2020, y cuya cobertura de búsqueda fue en las bases de datos: SCI.Expanded, SSCI, A&HCI, ESCI, CPCI-S, CPCI.SSH, BKCI-. y BKCI.SSH. Resultados. Se recuperaron y analizaron 49 documentos representados en 38 artículos, 7 textos de memorias de congresos, 2 capítulos de libro y 2 materiales editoriales. El conjunto de las investigaciones que tratan el tema de la CD y la CI sumaron 128 citas, 2.6 citas por documento e índice H: 7. Discusión.Conceptualmente, se encontró que para la CD y la CI su origen son los datos y que ambas disciplinas son predominantemente de carácter práctico. En aquellas investigaciones con mayor visibilidad hay participación multiautoral. La CD y la CI son áreas del conocimiento recientes en las cuales las tecnologías de la información son indispensables para el análisis de grandes cantidades de datos e información. Conclusiones. La CD y CI tienen un carácter intra y multi y transdisciplinar y se caracterizan por utilizar las tecnologías de la información para el análisis de grandes cantidades de datos e información.

Abstract Objective. To research scientific production at the thematic intersection of Data Science (DS) and Information Science (IS). Method. Informetric, descriptive study, and of first incursion, in the analysis of the written discourse of the academic texts included in the Web of Science (WoS), period 1900 to November 6, 2020, and coverage in the databases: SCI-Expanded, SSCI, A & HCI, ESCI, CPCI-S, CPCI-SSH, BKCI-S and BKCI-SSH. Results. 49 documents represented in 38 articles, 7 conference memoires, 2 book chapters and 2 editorial materials were retrieved and analyzed. The set of investigations that deal with the subject of DS and IS added 128 citations, 2.6 citations per document and H index: 7. Discussion. Conceptually, it was found that for DS and IS their origin is the data and that both disciplines are predominantly practical. In those investigations with greater visibility there are more than an author. DS and IS are recent areas of knowledge in which information technologies are indispensable for the analysis of large amounts of data and information. Conclusions. DS and IS have an intra- and multi and transdisciplinary character and they are characterized by the use of information technologies for the analysis of large amounts of data and information.

Ciência de dados e big data: o que isso significa para estudos populacionais e da saúde? / Data science and big data: what do these terms mean for population and health related studies?

Resumo Introdução O termo "big data" no ambiente acadêmico tem deixado de ser uma novidade, tornando-se mais comum em publicações científicas e em editais de fomento à pesquisa, levando a uma revisão profunda da ciência que se faz e se ensina. Objetivo Refletir sobre as possíveis mudanças que as ciências de dados podem provocar nas áreas de estudos populacionais e de saúde. Método Para fomentar esta reflexão, artigos científicos selecionados da área de big data em saúde e demografia foram contrastados com livros e outras produções científicas. Resultados Argumenta-se que o volume dos dados não é a característica mais promissora de big data para estudos populacionais e de saúde, mas a complexidade dos dados e a possibilidade de integração com estudos convencionais por meio de equipes interdisciplinares são promissoras. Conclusão No âmbito do setor de saúde e de estudos populacionais, as possibilidades da integração dos novos métodos de ciência de dados aos métodos tradicionais de pesquisa são amplas, incluindo um novo ferramental para a análise, monitoramento, predição de eventos (casos) e situações de saúde-doença na população e para o estudo dos determinantes socioambientais e demográficos.

Abstract Background The term big data is no longer new in the academic environment and has become more common in scientific publications and research grants, leading to a profound revision of the way science is being made and taught. Objective To reflect on the possible changes that data science can induce in population and health related studies. Method To foster this debate, scientific articles selected from the big data field in health and demography were contrasted with books and other scientific productions. Results It is argued that volume is not the most promising characteristic of big data for population and health related studies, but rather the complexity of data and the possibilities of integration with traditional studies by means of interdisciplinary teams. Conclusion In population and health related studies, the possibilities of integration between new and traditional methods are broad, and include new toolboxes for analysis, monitoring, prediction of events (cases) and health-disease processes in the population, and for the study of sociodemographic and environmental determinants.

Analítica de datos: incidencia de la contaminación ambiental en la salud pública en Medellín (Colombia) / Data Analytics: incidence of air pollution on public health in Medellin, Colombia

RESUMEN Objetivo Analizar el impacto de la contaminación del aire por material particulado PM2,5 y su relación con el número de asistencias a entidades de salud por enfermedades respiratorias por medio de analítica de datos. Métodos Se analizaron datos del Área Metropolitana de Medellín, Colombia, ciudad ubicada en un valle estrecho densamente poblado e industrializado y que ha presentado episodios críticos de contaminación en los últimos años. Se analizaron tres fuentes de datos: datos meteorológicos aportados por el SIATA (Sistema de Alerta Temprana de Medellín y el Valle de Aburrá); datos de contaminación por material particulado PM2,5 aportados por SIATA; y reportes de los RIPS (Registros Individuales de Prestación de Servicios de Salud) aportados por la Secretaría de Salud. Resultados Se evidenció la relación entre la concentración de PM2,5 con las asistencias médicas por los diagnósticos de IRA, EPOC y asma. En un episodio crítico de contaminación por PM2,5, se encontraron los siguientes retardos en la atención médica: entre 0 y 2 días para el IRA, 0 y 7 días para el EPOC y 0 y 5 días para el asma. Discusión Se encontraron coeficientes de correlación que evidencian la asociación de la concentración de PM2,5 con las asistencias por los diagnósticos de IRA, EPOC y asma. La mayor correlación entre las tres morbilidades se presentó para el asma. La variable meteorológica de mayor correlación con la variable objetivo es la temperatura del aire para el caso de EPOC y asma. En el caso de IRA, la variable con mayor correlación es la velocidad del viento. Por otro lado, el día de la semana es una variable de gran importancia a la hora de realizar un estudio de atenciones por enfermedades.

ABSTRACT Objective To analyze the impact of air pollution by PM2,5 particulate matter and its relationship with the number of attendances to health entities for respiratory diseases through data analytics. Methods Data from the Metropolitan Area of Medellín, Colombia, a city located in a densely populated and industrialized narrow valley and that has presented critical episodes of contamination in recent years, were analyzed. Three data sources were analyzed: meteorological data provided by SIATA (Early Warning System of Medellín and the Aburra Valley), PM2,5 particulate matter contamination data provided by SIATA, and RIPS reports (Individual Registers for the Provision of Health Services) provided by the health department. Results The relationship between the concentration of PM2,5 and medical care for the diagnoses of ARI, COPD and asthma was evidenced. In a critical episode of PM2,5 contamination, the following delays in medical care were found: between 0-2 days for IRA, 0-7 days for COPD, and 0-5 days for asthma. Discussion Correlation coefficients were found that show the association of the concentration of PM2,5 with the attendances for the diagnoses of ARI, COPD, and asthma. The highest correlation between the three morbidities was found for asthma. The meteorological variable with the highest correlation with the objective variable is air temperature in the case of COPD and asthma. In the case of IRA, the variable with the highest correlation is wind speed. On the other hand, the day of the week is a variable of great importance when carrying out a study of care for diseases.

A era digital na Antropologia Forense / A digital age in Forensic Anthropology / Una era digital en Antropología Forense

Introdução: a produção de dados 3D tem-se revelado uma ferramenta útil na investigação e aplicação nas Ciências Forenses, contudo os avanços tecnológicos nem sempre são acompanhados pela legislação e comités de ética. Objectivo: aprofundar o tema do ponto de vista da Antropologia Forense. Metodologia: revisão bibliográfica sumária e consulta ao sistema jurídico português. Resultados: observa-se um vazio legislativo e uma ausência de normas éticas sobre a produção de dados 3D. Conclusão: é essencial que a revolução tecnológica seja acompanhada por um sistema jurídico adequado e comissões de ética estruturadas para uma evolução sustentável da Ciência.

Introduction: the 3D data production has proven to be a useful tool for Forensic Sciences, however technological advances are not always accompanied by updates of the Legislation and Ethics Committees. Objective: to develop the discussion on the subject from the point of view of Forensic Anthropology. Methodology: a summary review of the literature and consultation of the Portuguese legal system. Results: it is observable a legislative void and an absence of ethical norms about the 3D data production. Conclusion: it is essential that the technological revolution is followed by an adequate legal system and structured ethics committees for a sustainable evolution of Science.

Introducción: la producción de datos 3D ha demostrado ser una herramienta útil en la investigación y aplicación en Ciencias Forenses, sin embargo, los avances tecnológicos no siempre van acompañados de Comités de Legislación y Ética. Objetivo: profundizar el tema desde el punto de vista de la Antropología Forense. Metodología: revisión bibliográfica resumida y consulta del sistema legal portugués. Resultados: hay un vacío legislativo y una ausencia de estándares éticos con respecto a la producción de datos 3D. Conclusión: es esencial que la revolución tecnológica vaya acompañada de un sistema legal adecuado y comisiones de ética estructuradas para una evolución sostenible de la Ciencia

Thoughts and practices on studies of membrane processes for Chinese materia medica based on data science / 中草药

Data science is a form of data-oriented science, which serves as a set of theories, methodologies and technologies for data exploration and analysis. Considering that membrane separation process for Chinese materia medica (CMM) manufacturing is a non-linear system, the data obtained regarding its process can be either multivariate, non-linear, strong noise, non-normally distributed, or non-evenly distributed. Due to the special features of data science, research has shed lights on its application in the scientific exploration and technological innovation in the complex membrane processes based on CMM system. There are a few key aspects of data science that are worth mentioning, making it competent as an analytical tool for CMM manufacturing integrated membrane processes. They can be recognized as the following, such as (1) the ability to precisely describe the mass transfer properties of CMM integrated membrane processes; (2) the dynamic description of membrane mass transfer process by molecular simulation; (3) the application of computational fluid dynamics simulation in membrane technology, and (4) the powerful and advanced data processing technology. This paper also reviewed some real case studies encountered by the authors during the investigation of membrane technology for CMM manufacturing based on data science.

Study on precise mechanism of Chinese patent medicine from perspective of activating data / 中国中药杂志

Traditional Chinese medicine(TCM) syndrome differentiation and treatment has a characteristic and advantageous efficacy in the prevention and treatment of major diseases(no matter for new or sudden infectious diseases or major chronic diseases). At present, the clinical application by Western medicine disease's name, stage, classification and other indications limits the role of TCM syndrome differentiation and treatment, and makes TCM difficult to play its advantages. Therefore, the therapeutic value and social value attribute of Chinese patent medicine after being launched in the market cannot be effectively demonstrated, or even generalized as adjuvants. Under the circumstances, it is difficult to put forward precise positioning different from chemical drugs, with fewer high-level and high-quality evidence-based evidences for precise positioning. The research on the pathological links and therapeutic mechanism of its effect on diseases is also less systematic. The development of biotechnology, such as genomics, has brought medicine into the era of precision, providing ideas and technical support for the exploration of syndrome biomarkers and the analysis of therapeutic mechanism with them as parameters. Digital China Think Tank Forum once mentioned that the development of sequencing technology provides 100% of human genetic code, while only 3% can understand it. Block data 4.0: activation data in the era of artificial intelligence puts forward the concept of activation data, which can be regarded as a theoretical hypothesis for big data, provides a new cognitive thinking and solution for increasingly prominent data paradox between bioinformation explosion and clinical big data, and is a bridge between cross-border data association and fusion. After deeply mining the dominant and recessive value of clinical data and histological data, we can make the pathogenesis of syndrome differentiation and treatment from dark knowledge to clear knowledge. Therefore, with Chinese patent medicine as the guide, the research on the efficacy and mechanism of precise positioning of traditional Chinese medicine after marketing is carried out, and the precise system of "syndrome, disease, function, pathological link and biological connotation" is constructed, which provides a powerful basis and support for increasing the scientific and technological content of varieties.

Microbiome data science

Best practices from open data science are spreading across research fields, providing new opportunities for research andeducation. Open data science emphasizes the view that digitalization is enabling new forms of resource sharing, collaboration and outreach. This has the potential to improve the overall transparency and efficiency of research. Microbiomebioinformatics is a rapidly developing area that can greatly benefit from this progress. The concept of microbiome datascience refers to the application of best practices from open data science to microbiome bioinformatics. The increasingavailability of open data and new opportunities to collaborate online are greatly facilitating the development of this field. Amicrobiome data science ecosystem combines experimental research data with open data processing and analysis andreproducible tutorials that can also serve as an educational resource. Here, we provide an overview of the current status ofmicrobiome data science from a community developer perspective and propose directions for future development of thefield.

Los Big Data: conceptos relacionados y algunas aplicaciones en pediatría / The Big Data: related concepts and some applications in pediatrics

Resumen: El avance de la tecnología médica, el registro de salud electrónico (EHR, por sus siglas en inglés) y la producción compleja de datos biomoleculares están generando grandes volúmenes de información, en varios formatos y de múltiples fuentes, que se conocen como "Big Data". El análisis integrado de estos datos ha abierto una amplia posibilidad para explorar respuestas a problemas de salud. En pediatría, se han incrementado los estudios se analizan Big Data o se utilizan las herramientas infor máticas que se han desarrollado para analizar estos datos. Los propósitos de esos estudios han sido variados, por ejemplo: en la detección y prevención temprana de una amplia gama de afecciones médicas, mejoramiento de los diagnósticos, para especificar tratamientos o anticipar el resultado de alguna patología, etc. El presente documento tiene como objetivo revisar los conceptos principales involucrados en el análisis de Big Data o en las tecnologías informáticas asociadas, así como también examinar sus aplicaciones, potencialidades y limitaciones actuales. Este estudio se realizó sobre la base de una revisión bibliográfica no sistemática, centrada en el campo de la pediatría. En la selección de los ejemplos de aplicación, se consideró que eran fuentes primarias, publicadas en los últimos cinco años y con poblaciones infanto-juveniles.

Abstract: Medical technology advances, the Electronic Health Record (EHR), and the complex production of biomolecular data are generating large volumes of information, in various formats and from multiple sources, which are known as "Big Data". The integrated analysis of these data has opened up a wide possibility to explore answers related to health problems. In pediatrics, there has been an increase in the studies on Big Data, or the computer tools use that have been developed to analyze these data. The purposes of those studies have been diverse, for example, for earlier detection and prevention of a wide range of medical conditions, improvement of diagnoses, to specify treatments or anticipate the outcome of some pathology and so on. For this reason, this contribution aims to review the main concepts involved in the analysis of Big Data or its related computer technologies, as well as to exa mine their current applications, potentialities, and limitations. This study was carried out based on a non-systematic bibliographical review, focused on the field of pediatrics. In the selection of applica tion examples, it was considered that they were primary sources, published in the last five years and with child and youth populations.

Data science in large cohort studies / 中华流行病学杂志

Large cohort study gained its popularity in biomedical research and demonstrated its application in exploring disease etiology and pathogenesis,improving the prognosis of disease,as well as reducing the burden of diseases.Data science is an interdisciplinary field that uses scientific methods from computer science and statistics to extract insights or knowledge from data in a specific domain.The results from the combination of the two would provide new evidence for developing the strategies and measures on disease prevention and control.This review included a brief introduction of data science,descriptions on characteristics of large cohort data according to the development of the study design,and application of data science at each stage of a large cohort study,as well as prospected the application of data science in the future large cohort studies.

Data science in large cohort studies / 中华流行病学杂志

Large cohort study gained its popularity in biomedical research and demonstrated its application in exploring disease etiology and pathogenesis,improving the prognosis of disease,as well as reducing the burden of diseases.Data science is an interdisciplinary field that uses scientific methods from computer science and statistics to extract insights or knowledge from data in a specific domain.The results from the combination of the two would provide new evidence for developing the strategies and measures on disease prevention and control.This review included a brief introduction of data science,descriptions on characteristics of large cohort data according to the development of the study design,and application of data science at each stage of a large cohort study,as well as prospected the application of data science in the future large cohort studies.

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

Evaluation and Interpretation of 9 Body Constitution Scores of CCMQ-J by Seven Independent Questionnaires / 日本補完代替医療学会誌

In this study, we proposed an approach to interpret the classification of body constitution based on the Japanese Version of Constitution in Chinese Medicine Questionnaire (CCMQ-J) in terms of an augmented questionnaire combining seven independent questionnaires. The augmented questionnaire consists of 254 questions in terms of seven categories of attributes, which are the (i) basic information (BI), (ii) disease (DI), (iii) social factors (SO), (iv) mental factors (ME), (v) dietary habits (DH), (vi) sleeping state (SL), and (vii) sub-health (SH). The partial least square (PLS) regression has been adopted to model the correlations between the scores of body constitutions and the questions, and their results show that the body constitution can be represented by the linear combination of the questions substantially (correlation coefficients between the true and predicted constitutions are all above 0.7). Moreover, by using the crowdsourcing technique in data collection, a total of 851 samples (350 males and 501 females between 20 and 85 years old) samples with diverse geographical backgrounds in Japan have been collected, from which new medical implications have been extracted through the discussion in a Traditional Chinese Medicine standpoint. This study serves as a crucial step for validating the philosophy of ancient Chinese medicine by the state-of-the-art information science techniques and facilitating the use of the CCMQ-J in public healthcare.