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1.
Rev. bras. med. esporte ; 29: e2022_0152, 2023. tab, graf
Article in English | LILACS | ID: biblio-1394837

ABSTRACT

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes' training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes' explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes' training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.


RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.


RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.


Subject(s)
Humans , Artificial Intelligence , Physical Fitness/physiology , Neural Networks, Computer , Athletic Performance/physiology , Athletes
2.
São Paulo; s.n; 20220720. 50 p.
Thesis in Portuguese | LILACS, BBO | ID: biblio-1379730

ABSTRACT

Nas últimas décadas a sociedade como um todo foi impactada das mais diversas maneiras pelo uso da tecnologia. Inteligência Artificial é o termo usado para se referir a algoritmos que permitem com que computadores realizem tarefas que exigem a percepção humana para serem feitas. O presente estudo tem o objetivo de relatar o que há de mais recente na literatura sobre o tema de Inteligência Artificial na Odontologia, mais propriamente na área de Patologia Oral e Maxilofacial, e realizar um piloto para diferenciação de lesões utilizando software gratuito. Para realizar o estudo, foram selecionados casos específicos dos últimos 20 anos do Serviço de Patologia Oral e Maxilofacial da Universidade de São Paulo, sendo estes casos de lesões benignas e malignas para comparação e poder elucidar como a patologia através da IA pode ser realizada.). Ao finalizar este processo, o modelo de ML identificou as imagens que foram pedidas para que o software analisasse e uma taxa de acurácia foi obtida, que neste caso foi de 98% de assertividade de que a imagem colocada era referente a uma imagem histológica de uma lesão maligna e de 97% para a lesão benigna (neste caso, um carcinoma epidermoide e um fibroma respectivamente). O estudo concluiu que a IA é pouca explorada tanto no âmbito rotineiro quanto acadêmico e que novas pesquisas devem ser realizadas com incentivo a fim de produzir literatura, mostrar aos estudantes o que pode ser realizado com tecnologia apropriada e agilizar o processo diagnóstico, facilitando a vida do profissional e de todo o sistema que o envolve.


Subject(s)
Pathology, Oral , Artificial Intelligence , Biomedical Technology
3.
RECIIS (Online) ; 16(2): 266-280, abr.-jun. 2022. ilus
Article in Portuguese | LILACS | ID: biblio-1378355

ABSTRACT

Este trabalho tem como objetivo relatar estratégias para coleta de um conjunto de dados em português para treinamento de modelos de Inteligência Artificial com vistas a identificar de forma automática fake news sobre covid-19 disseminadas durante a pandemia, a partir de código Python. Analisamos um método de detecção de fake news baseado em uma Rede Neural Recorrente e de aprendizagem supervisionada. Selecionamos um corpus com 7,2 mil textos coletados em websites e agências de notícias por Monteiro et al. (2018) com cada um previamente catalogado como verdadeiro ou falso como conjunto de dados de treino e validação. O modelo foi usado para detecção de fake news sobre covid-19 em um conjunto de notícias coletadas e classificadas pelos autores deste trabalho. O índice de acerto foi de 70%, ou seja, essa foi a taxa de sucesso da detecção dos itens catalogados.


This work aims to report strategies for collecting a dataset in Portuguese for training Artificial Intelligence models to automatically identify fake news about covid-19 disseminated during the pandemic, using Python code. We analyze a fake news detection method based on a Recurrent Neural Network and supervised learning. We selected a corpus with 7,200 texts collected on websites and news agencies by Monteiro et al. (2018), each one of them previously cataloged as true or false as a training and validation dataset. This model was used to detect fake news about covid-19 in a set of news collected and classified by the authors of this work. The hit rate was 70%.


Este trabajo tiene como objetivo informar estrategias para recopilar un conjunto de datos en portugués para entrenar modelos de Inteligencia Artificial para identificar automáticamente noticias falsas sobre covid-19 difundidas durante la pandemia, utilizando el código Python. Analizamos un método de detección de noticias falsas basado en una Red Neuronal Recurrente y de aprendizaje supervisado. Seleccionamos un corpus de 7.200 textos recogidos en webs y agencias de noticias por Monteiro et al. (2018) con cada uno catalogado previamente como verdadero o falso como un conjunto de datos de entrenamiento y validación. El modelo se utilizó para detectar noticias falsas sobre covid-19 en un conjunto de noticias recopiladas y clasificadas por los autores de este trabajo. La tasa de acierto fue del 70%, es decir, esta fue la tasa de éxito de detección de los artículos catalogados.


Subject(s)
Humans , Programming Languages , Artificial Intelligence , Communication , COVID-19 , Disinformation , Data Collection , News , Health Information Exchange
5.
Rev. adm. pública (Online) ; 56(3): 426-440, mai.-jun. 2022. tab, graf
Article in Portuguese | LILACS | ID: biblio-1387590

ABSTRACT

Resumo A evasão fiscal é a consequência da prática da sonegação. Apenas no Brasil, estima-se que ela corresponda a 8% do PIB. Com isso, os governos necessitam de sistemas inteligentes para apoiar os auditores fiscais na identificação de sonegadores. Tais sistemas dependem de dados sensíveis dos contribuintes para o reconhecimento dos padrões, que são protegidos por lei. Com isso, o presente trabalho apresenta uma solução inteligente, capaz de identificar os perfis de potenciais sonegadores com o uso apenas de dados abertos, públicos, disponibilizados pela Receita Federal e pelo Conselho Administrativo Tributário do Estado de Goiás, entre outros cadastros públicos. Foram gerados três modelos que utilizaram os recursos Random Forest, Redes Neurais e Grafos. Em validação depois de melhorias finas, foi possível obter acurácia superior a 98% na predição do perfil inadimplente. Por fim, criou-se uma solução de software visual para uso e validação pelos auditores fiscais do estado de Goiás.


Resumen La evasión fiscal es la consecuencia de la práctica de la defraudación tributaria. En Brasil, se estima que corresponde al 8% del PIB. Por lo tanto, los gobiernos necesitan y utilizan sistemas inteligentes para ayudar a los agentes de hacienda a identificar a los defraudadores fiscales. Dichos sistemas se basan en datos confidenciales de los contribuyentes para el reconocimiento de patrones, que están protegidos por ley. Este trabajo presenta una solución inteligente, capaz de identificar perfiles de potenciales defraudadores fiscales, utilizando únicamente datos públicos abiertos, puestos a disposición por la Hacienda Federal y por el Consejo Administrativo Tributario del Estado de Goiás, entre otros registros públicos. Se generaron tres modelos utilizando random forest y neural networks. En la validación después de finas mejoras, fue posible obtener una precisión superior al 98% en la predicción del perfil moroso. Finalmente, se creó una solución de software visual para uso y validación por parte de los auditores fiscales del estado de Goiás.


Abstract Tax evasion is the practice of the non-payment of taxes. In Brazil alone, it is estimated as 8% of GDP. Thus, governments must use intelligent systems to support tax auditors to identify tax evaders. Such systems seek to recognize patterns and rely on sensitive taxpayer data that is protected by law and difficult to access. This research presents a smart solution, capable of identifying the profile of potential tax evaders, using only open and public data, made available by the Brazilian internal revenue service, the administrative council of tax appeals of the State of Goiás, and other public sources. Three models were generated using Random Forest, Neural Networks, and Graphs. The validation after fine improvements offered an accuracy greater than 98% in predicting tax evading companies. Finally, a web-based solution was created to be used and validated by tax auditors of the State of Goiás.


Subject(s)
Taxes , Artificial Intelligence
6.
Rev. bioét. (Impr.) ; 30(1): 82-93, jan.-mar. 2022.
Article in Portuguese | LILACS | ID: biblio-1376482

ABSTRACT

Resumo Este artigo explora vantagens e possíveis desafios bioéticos do uso da inteligência artificial em hospitais. A partir da identificação de desafios no desenvolvimento de sistemas dotados de inteligência artificial (fase pré-hospitalar) e na implementação e capacitação de equipes de saúde (fase hospitalar), analisa-se o papel da abordagem bioética no enfrentamento dessa situação, sobretudo dos comitês de bioética hospitalar. Desse modo, mediante a identificação de desafios de ordem individual - referentes à autonomia, consentimento e privacidade dos pacientes - e coletiva - como a sociedade em geral deve se portar diante das novas tecnologias -, observa-se o papel do Estado na proteção da privacidade do paciente no contexto de utilização da inteligência artificial. Em conclusão, considerando a vulnerabilidade humana perante a tecnologia, entende-se que a regulamentação é um instrumento que, junto com os princípios bioéticos, tenta minimizar os desafios do uso da inteligência artificial em hospitais.


Abstract This paper explores advantages and possible bioethical challenges of using artificial intelligence in hospitals. By identifying challenges both in the development of artificial intelligence systems (pre-hospital phase), its adoption, and training of healthcare teams (hospital phase), it analyzes the role of the bioethical approach in addressing this situation, especially in hospital bioethics committees. Hence, by identifying individual - related to autonomy, consent and patient privacy -, and collective challenges - how society at large should behave before new technologies -, the paper examines the role of the state in protecting patient privacy in contexts where artificial intelligence is used. In conclusion, considering the human vulnerability before technology, regulation is a tool that, anchored in bioethical principles, aims to minimize the challenges concerning artificial intelligence in hospitals.


Resumen Este artículo explora las ventajas y los posibles desafíos bioéticos que plantea el uso de la inteligencia artificial en los hospitales. Con base en la identificación de los desafíos en el desarrollo de sistemas dotados de inteligencia artificial (etapa prehospitalaria) y en la implementación y capacitación de los equipos de salud (etapa hospitalaria), se analiza el papel del enfoque bioético en el enfrentamiento de esta situación, especialmente de los comités de bioética hospitalaria. Por lo tanto, mediante la identificación de los desafíos individuales -relativos a la autonomía, al consentimiento y a la privacidad de los pacientes- y colectivos -cómo debe actuar la sociedad en general ante las nuevas tecnologías-, se observa el papel del Estado en la protección de la privacidad del paciente en el contexto del uso de la inteligencia artificial. En conclusión, teniendo en cuenta la vulnerabilidad humana ante la tecnología, se entiende que la regulación es un instrumento que, junto con los principios bioéticos, trata de minimizar los desafíos del uso de la inteligencia artificial en los hospitales.


Subject(s)
Social Control, Formal , Technology , Bioethics , Artificial Intelligence , State , Hospitals , Human Rights
7.
Rev. Hosp. Ital. B. Aires (2004) ; 42(1): 12-20, mar. 2022. graf, ilus, tab
Article in Spanish | LILACS, BINACIS, UNISALUD | ID: biblio-1368801

ABSTRACT

Introducción: determinar la causa de muerte de los pacientes internados con enfermedad cardiovascular es de suma importancia para poder tomar medidas y así mejorar la calidad su atención y prevenir muertes evitables. Objetivos: determinar las principales causas de muerte durante la internación por enfermedades cardiovasculares. Desarrollar y validar un algoritmo para clasificar automáticamente a los pacientes fallecidos durante la internación con enfermedades cardiovasculares Diseño del estudio: estudio exploratorio retrospectivo. Desarrollo de un algoritmo de clasificación. Resultados: del total de 6161 pacientes, el 21,3% (1316) se internaron por causas cardiovasculares; las enfermedades cerebrovasculares representan el 30,7%, la insuficiencia cardíaca el 24,9% y las enfermedades cardíacas isquémicas el 14%. El algoritmo de clasificación según motivo de internación cardiovascular vs. no cardiovascular alcanzó una precisión de 0,9546 (IC 95%: 0,9351-0,9696). El algoritmo de clasificación de causa específica de internación cardiovascular alcanzó una precisión global de 0,9407 (IC 95%: 0,8866-0,9741). Conclusiones: la enfermedad cardiovascular representa el 21,3% de los motivos de internación de pacientes que fallecen durante su desarrollo. Los algoritmos presentaron en general buena performance, particularmente el de clasificación del motivo de internación cardiovascular y no cardiovascular y el clasificador según causa específica de internación cardiovascular. (AU)


Introduction: determining the cause of death of hospitalized patients with cardiovascular disease is of the utmost importance in order to take measures and thus improve the quality of care of these patients and prevent preventable deaths. Objectives: to determine the main causes of death during hospitalization due to cardiovascular diseases.To development and validate a natural language processing algorithm to automatically classify deceased patients according to their cause for hospitalization. Design: retrospective exploratory study. Development of a natural language processing classification algorithm. Results: of the total 6161 patients in our sample who died during hospitalization, 21.3% (1316) were hospitalized due to cardiovascular causes. The stroke represent 30.7%, heart failure 24.9%, and ischemic cardiac disease 14%. The classification algorithm for detecting cardiovascular vs. Non-cardiovascular admission diagnoses yielded an accuracy of 0.9546 (95% CI 0.9351, 0.9696), the algorithm for detecting specific cardiovascular cause of admission resulted in an overall accuracy of 0.9407 (95% CI 0.8866, 0.9741). Conclusions: cardiovascular disease represents 21.3% of the reasons for hospitalization of patients who die during hospital stays. The classification algorithms generally showed good performance, particularly the classification of cardiovascular vs non-cardiovascular cause for admission and the specific cardiovascular admission cause classifier. (AU)


Subject(s)
Humans , Artificial Intelligence/statistics & numerical data , Cerebrovascular Disorders/mortality , Myocardial Ischemia/mortality , Heart Failure/mortality , Hospitalization , Quality of Health Care , Algorithms , Reproducibility of Results , Factor Analysis, Statistical , Mortality , Cause of Death , Electronic Health Records
8.
Int. j. cardiovasc. sci. (Impr.) ; 35(1): 127-134, Jan.-Feb. 2022. graf
Article in English | LILACS | ID: biblio-1356306

ABSTRACT

Abstract Cardiovascular diseases are the leading cause of death in the world. People living in vulnerable and poor places such as slums, rural areas and remote locations have difficulty in accessing medical care and diagnostic tests. In addition, given the COVID-19 pandemic, we are witnessing an increase in the use of telemedicine and non-invasive tools for monitoring vital signs. These questions motivate us to write this point of view and to describe some of the main innovations used for non-invasive screening of heart diseases. Smartphones are widely used by the population and are perfect tools for screening cardiovascular diseases. They are equipped with camera, flashlight, microphone, processor, and internet connection, which allow optical, electrical, and acoustic analysis of cardiovascular phenomena. Thus, when using signal processing and artificial intelligence approaches, smartphones may have predictive power for cardiovascular diseases. Here we present different smartphone approaches to analyze signals obtained from various methods including photoplethysmography, phonocardiograph, and electrocardiography to estimate heart rate, blood pressure, oxygen saturation (SpO2), heart murmurs and electrical conduction. Our objective is to present innovations in non-invasive diagnostics using the smartphone and to reflect on these trending approaches. These could help to improve health access and the screening of cardiovascular diseases for millions of people, particularly those living in needy areas.


Subject(s)
Artificial Intelligence/trends , Cardiovascular Diseases/diagnosis , Triage/trends , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/trends , Smartphone/trends , Triage/methods , Telemedicine/methods , Telemedicine/trends , Mobile Applications/trends , Smartphone/instrumentation , Telecardiology , COVID-19/diagnosis
9.
Curitiba; s.n; 20220224. 277 p. ilus, tab.
Thesis in Portuguese | LILACS, BDENF | ID: biblio-1370518

ABSTRACT

Resumo: Introdução: A Sistematização da Assistência de Enfermagem corresponde à organização do trabalho quanto ao método, pessoal e instrumentos, vislumbrando operacionalizar o processo de enfermagem. Porém, há limitação da compreensão semântica do seu significado, conhecimento, operacionalização dos seus componentes e da contribuição para prática profissional e Ciência da Enfermagem. Objetivo: analisar, sob a ótica da Teoria da Complexidade, a construção de um modelo ontológico sobre Sistematização da Assistência de Enfermagem como tecnologia de apoio à organização da prática profissional do enfermeiro. Método: estudo qualitativo e exploratório, em três etapas. Primeiramente, construiu-se um mapa conceitual baseado nas sete etapas apresentadas por Cañas, Novak, Reiska (2015), almejando identificar conceitos, estrutura, processos e operação da Sistematização da Assistência de Enfermagem, à luz da Teoria da Complexidade. Organizou-se e representou-se o conhecimento com apoio do software CMap Tools. A segunda etapa compôs-se de entrevistas semiestruturadas, entre maio e dezembro de 2020, com 17 enfermeiros, dos quais nove eram do Grupo de Trabalho da Sistematização da Prática de Enfermagem da Comissão Mista da Associação Brasileira de Enfermagem e Conselho Regional de Enfermagem-PR e oito da Comissão Permanente de Sistematização da Prática de Enfermagem, nomeada pela Associação Brasileira de Enfermagem. Empregou-se a Análise de Conteúdo Temática, apoiada no software MAXQDA. Na terceira etapa, modelou-se a representação de uma ontologia sobre a Sistematização da Assistência de Enfermagem, baseada no guia interativo Ontology Development 101 apoiada pelo software Protégé (versão 5.5.0), a partir do mapa conceitual e das entrevistas. Resultados: identificou-se inconsistência semântica e de correlações, retratando a complexidade dos componentes da Sistematização da Assistência de Enfermagem, com fragmentos mecanicistas. No mapa conceitual, elaboraram-se três camadas conceituais. Organizaram-se os conceitos de acordo com a proposta conceitual da Sistematização da Assistência de Enfermagem prevista em sua principal legislação e posteriormente foram ampliados. Desta análise, procedeu-se ao agrupamento por temáticas: Sistematização da Assistência de Enfermagem; Ações de Enfermagem; Ações da Gestão do Cuidado; Ações de Gestão do Serviço de Enfermagem; Ações para Aplicação dos Cuidados; Ações para Aplicação no Serviço de Enfermagem; Fundamentos; Competências; Instrumentos; Normativas e Pessoal. Das entrevistas, emergiram 863 unidades de registro e seis categorias: Significado de Sistematização da Assistência de Enfermagem, com três subcategorias primárias; Construção Histórica do Conceito de Sistematização da Assistência de Enfermagem, com quatro subcategorias primárias; Ensino e Aprendizagem; Pesquisa da Enfermagem; Implicações Prática e Concretização da Sistematização da Assistência de Enfermagem. Identificaram-se 156 conceitos relevantes para modelagem da ontologia, utilizando-se da "metodologia 101", objetivando representar o conhecimento do domínio Sistematização da Assistência de Enfermagem. Considerações finais: a ontologia sobre Sistematização da Assistência de Enfermagem ancorada na Teoria da Complexidade permitiu um novo olhar sobre os fenômenos, os quais devem ser desenvolvidos, revistos e ressignificados. Acredita-se que esta ontologia facilite a representação formal do conhecimento sobre Sistematização da Assistência de Enfermagem, afirmando-a enquanto área de conhecimento representativo, fortalecendo sua identidade, significado unívoco, organização, compartilhamento de saberes e de informação. Ademais, pode favorecer difusão de vocabulário comum, contribuindo com a prática profissional de enfermeiros.


Abstract: Introduction: the Systematization of Nursing Care is the work organization according to the method, personnel and instruments, which glimpses to operationalize the nursing process. However, there is a limitation in the semantic understanding of its meaning, knowledge, and operationalization of its components and the contribution to the practice and science of nursing. Objective: to analyze, from the perspective of Complexity Theory, the process of building an ontological model on Systematization of Nursing Care as a technology to support the organization of professional nursing practice. Method: qualitative and exploratory study, in three stages. Firstly, a conceptual map was built based on the seven stages presented by Cañas, Novak, Reiska (2015), aiming to identify concepts, structure, processes and operation of the Systematization of Nursing Care, in light of the complexity, anchored in the related literature. Knowledge was organized and represented with the support of CMap Tools software. The second stage consisted of semi-structured interviews, between May and December 2020, done with 17 professionals, of whom nine from the Working Group on the Systematization of Nursing Practice of the Mixed Commission of the Brazilian Nursing Association and Regional Nursing Council-PR and eight from the Permanent Commission for the Systematization of Nursing Practice, appointed by the Association. Thematic Content Analysis was used, supported by the MAXQDA software. In the third stage, the representation of ontology on the Systematization of Nursing Care was modeled, based on the interactive guide Ontology Development 101 supported by the software Protégé (version 5.5.0), from the conceptual map and the interviews. Results: semantic inconsistency and correlations were identified, portraying the complexity of the components of the Systematization of Nursing Care, with mechanistic fragments. In the conceptual map, three conceptual layers were elaborated. The concepts were organized according to the conceptual proposal of the Systematization of Nursing Care provided for in its main legislation and were later expanded. From this analysis, we proceeded to group by themes: Systematization of Nursing Care; Nursing Actions; Management Care Actions; Nursing Service Management Actions, Care Management Actions; Nursing Service Management Actions; Actions for Application of Care, and Actions for Application in the Nursing Service; Fundamentals, Competencies; Instruments; Regulations and Personnel. From the interviews, 863 record units and six categories emerged: Meaning of Systematization of Nursing Care, with three primary subcategories; Historical Construction of the Concept of Systematization of Nursing Care, with four primary subcategories; Teaching and Learning; Nursing Research; Practical Implications and Implementation of the Systematization of Nursing Care. 156 relevant concepts for ontology modeling were identified using the "101 methodology", aiming to represent the knowledge of the Systematization of Nursing Care domain. Final considerations: the ontology on Systematization of Nursing Care anchored in Complexity Theory allowed a new look at the phenomena, which must be developed, reviewed and re-signified. It is believed that this ontology facilitates the formal representation of knowledge about Systematization of Nursing Care, affirming it as a representative area of knowledge, strengthening its identity, univocal meaning, organization, sharing of knowledge and information. Furthermore, it can favor the diffusion of common vocabulary, contributing to the professional practice of nurses.


Resumen: Introducción: la sistematización de la asistencia de Enfermeríaes la organización del trabajo en cuanto a método, personal e instrumentos, con el objetivo de operacionalizar el proceso de enfermería. Sin embargo, existe una limitación en la comprensión semántica de su significado, conocimiento, operacionalización de sus componentes y el aporte a la práctica y ciencia de enfermería. Objetivo: analizar, en la perspectiva de la Teoría de la Complejidad, el proceso de construcción de un modelo ontológico sobre la sistematización de la asistencia de Enfermería como tecnología de apoyo a la organización de la práctica profesional de enfermería. Método: estudio cualitativo y exploratorio, en tres etapas. En primer lugar, se construyó un mapa conceptual a partir de las siete etapas proclamadas presentadas por Cañas, Novak, Reiska (2015), con el objetivo de identificar conceptos, estructura, procesos y funcionamiento de la sistematización de la asistencia de Enfermería, a la luz de la complejidad, anclada en la literatura relacionada. El conocimiento fue organizado y representado con el apoyo del software CMap Tools. La segunda etapa consistió en entrevistas semiestructuradas, entre mayo y diciembre de 2020, con 17 profesionales, de los cuales nueve del Grupo de Trabajo sobre Sistematización de la Práctica de Enfermería de la Comisión Mixta de la Asociación Brasileña de Enfermería y Consejo Regional de Enfermería-PR y ocho del Comisión Permanente para la Sistematización de la Práctica de Enfermería, designada por el Colegio. Se utilizó el Análisis de Contenido Temático, apoyado en el software MAXQDA. En la tercera etapa, se modeló la representación de una ontología sobre la sistematización de la asistencia de Enfermería, a partir de la guía interactiva Ontology Development 101 con el apoyo del software Protégé (versión 5.5.0), del mapa conceptual y de las entrevistas. Resultados: fueron identificadas inconsistencias semánticas y correlaciones, retratando la complejidad de los componentes de lasistematización de la asistencia de Enfermería, con fragmentos mecanicistas. En el mapa conceptual se elaboraron tres capas conceptuales. Los conceptos fueron organizados de acuerdo con la propuesta conceptual de la sistematización de la asistencia de Enfermeríaprevista en su legislación principal y posteriormente fueron ampliados. A partir de ese análisis, se procedió a agrupar por temas: Sistematización de la Asistencia de Enfermería; Acciones de Enfermería; Acciones de Gestión del Cuidado; Acciones de Gestión del Servicio de Enfermería; Acciones de Gestión del Cuidado; Acciones de Gestión del Servicio de Enfermería; Acciones de Aplicación de Cuidados; Acciones de Aplicación en el Servicio de Enfermería; Fundamentos; Competencias; Instrumentos; Reglamentos y Personal. De las entrevistas surgieron 863 unidades de registro y seis categorías: Significado de sistematización de la asistencia de Enfermería, con tres subcategorías primarias; Construcción Histórica del Concepto de Sistematización de la Atención de Enfermería, con cuatro subcategorías primarias; Enseñanza y Aprendizaje; Investigación en Enfermería; Implicaciones Prácticas e Implementación de la sistematización de la asistencia de Enfermería. Fueron identificados 156 conceptos relevantes para el modelado ontológico utilizando la "metodología 101", con el objetivo de representar el conocimiento del dominio sistematización de la asistencia de Enfermería. Consideraciones finales: la ontología sobre sistematización de la asistencia de Enfermería anclado en la Teoría de la Complejidad permitió una nueva mirada sobre los fenómenos, que deben ser desarrollados, revisados y redefinidos. Se cree que esta ontología facilita la representación formal del conocimiento sobre sistematización de la asistencia de Enfermería, afirmándola como área representativa del saber, fortaleciendo su identidad, sentido unívoco, organización, intercambio de saberes e informaciones. Además, puede favorecer la difusión del vocabulario común, contribuyendo a la práctica profesional de los enfermeros.


Subject(s)
Humans , Male , Female , Patient Care Management , Artificial Intelligence , Vocabulary, Controlled , Practice Management , Nursing Care
11.
Article in Chinese | WPRIM | ID: wpr-936186

ABSTRACT

Objective: To explore the types and clinical characteristics of chronic rhinosinusitis with nasal polyps (CRSwNP) based on artificial intelligence and whole-slide imaging (WSI), and to explore the consistency of the diagnostic criteria of the Japanese epidemiological survey of refractory eosinophilic chronic rhinosinusitis (JESREC) in Chinese CRSwNP patients. Methods: The data of 136 patients with CRSwNP (101 males and 35 females, aging 14 to 70 years) who underwent endoscopic sinus surgery from 2018 to 2019 in the Department of Otorhinolaryngology Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University were analysed retrospectively. The preoperative clinical characteristics of patients were collected, such as visual analogue scale (VAS) of nasal symptoms, peripheral blood inflammatory cell count, total immunoglobulin E (IgE), Lund-Kennedy score and Lund-Mackay score. The proportion of inflammatory cells such as eosinophils, lymphocytes, plasma cells and neutrophils were calculated on the WSI of each patient through artificial intelligence chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0), and the specific type of nasal polyps was then obtained as eosinophilic CRSwNP (eCRSwNP) or non-eosinophilic CRSwNP (non-eCRSwNP). In addition, the JESREC diagnostic criteria was used to classify the nasal polyps, and the classification results were compared with the current gold standard for nasal polyps diagnosis (pathological diagnosis based on WSI). The accuracy, sensitivity and specificity of the diagnostic criteria of JESREC were evaluated. The data were expressed in M (Q1, Q3) and statistically analyzed by SPSS 17.0. Results: There was no significant difference between eCRSwNP and non-eCRSwNP in age distribution, gender, time of onset, total VAS score, Lund-Kennedy score or Lund-Mackay score. However, there was a significant difference in the ratio of nasal polyp inflammatory cells (eosinophils 40.5% (22.8%, 54.7%) vs 2.5% (1.0%, 5.3%), neutrophils 0.3% (0.1%, 0.7%) vs 1.3% (0.5%, 3.6%), lymphocytes 49.9% (39.3%, 65.9%) vs 82.0% (72.8%, 87.5%), plasma cells 5.1% (3.6%, 10.5%) vs 13.0% (7.4%, 16.3%), χ2 value was 9.91, 4.66, 8.28, 5.06, respectively, all P<0.05). In addition, eCRSwNP had a significantly higher level of proportion of allergic symptoms (nasal itching and sneezing), asthma, peripheral blood eosinophil and total IgE (all P<0.05). The overall accuracy, sensitivity and specificity of the JESREC diagnostic criteria was 74.3%, 81.3% and 64.3%, respectively. Conclusions: The eCRSwNP based on artificial intelligence and WSI has significant high level of allergic symptoms, asthma, peripheral blood eosinophils and total IgE, and the percentages of inflammatory cells in nasal polyps are different from that of non-eCRSwNP. The JESREC diagnostic criteria has good consistency in our research.


Subject(s)
Artificial Intelligence , Chronic Disease , Eosinophils/metabolism , Female , Humans , Male , Nasal Polyps/pathology , Retrospective Studies , Rhinitis/pathology , Sinusitis/pathology
12.
Article in Chinese | WPRIM | ID: wpr-936105

ABSTRACT

According to the fourth national oral health epidemiological survey report (2018), billions of teeth are lost or missing in China, inducing chewing dysfunction, which is necessary to build physiological function using restorations. Digital technology improves the efficiency and accuracy of oral restoration, with the application of three-dimensional scans, computer-aided design (CAD), computer-aided manufacturing (CAM), bionic material design and so on. However, the basic research and product development of digital technology in China lack international competitiveness, with related products basically relying on imports, including denture 3D design software, 3D oral printers, and digitally processed materials. To overcome these difficulties, from 2001, Yuchun Sun's team, from Peking University School and Hospital of Stomatology, developed a series of studies in artificial intelligence design and precision bionics manufacturing of complex oral prostheses. The research included artificial intelligence design technology for complex oral prostheses, 3D printing systems for oral medicine, biomimetic laminated zirconia materials and innovative application of digital prosthetics in clinical practice. The research from 2001 to 2007 was completed under the guidance of Prof. Peijun Lv and Prof. Yong Wang. Under the support of the National Natural Science Foundation of China, the National Science and Technology Support Program, National High-Tech R & D Program (863 Program) and Beijing Training Project for the Leading Talents in S & T, Yuchun Sun's team published over 200 papers in the relevant field, authorized 49 national invention patents and 1 U.S. invention patent and issued 2 national standards. It also developed 8 kinds of core technology products in digital oral prostheses and 3 kinds of clinical diagnosis and treatment programs, which significantly improved the design efficiency of complex oral prostheses, the fabrication accuracy of metal prostheses and the bionic performance of ceramic materials. Compared with similar international technologies, the program doubled the efficiency of bionic design and manufacturing accuracy and reduced the difficulty of diagnosis and cost of treatment and application by 50%, with the key indicators of those products reaching the international leading level. This program not only helped to realize precision, intelligence and efficiency during prostheses but also provided functional and aesthetic matches for patients after prostheses. The program was rewarded with the First Technical Innovation Prize of the Beijing Science and Technology Awards (2020), Gold Medal of Medical Research Group in the First Medical Science and Technology Innovation Competition of National Health Commission of the People's Republic of China (2020) and Best Creative Award in the First Translational Medical Innovation Competition of Capital (2017). This paper is a review of the current situation of artificial intelligence design and precision bionics manufacturing of complex oral prosthesis.


Subject(s)
Artificial Intelligence , Bionics , Computer-Aided Design , Dental Prosthesis Design , Humans , Printing, Three-Dimensional , Prostheses and Implants
13.
Article in Chinese | WPRIM | ID: wpr-936084

ABSTRACT

Objective: To establish a neural network model for predicting lymph node metastasis in patients with stage II-III gastric cancer. Methods: Case inclusion criteria: (1) gastric adenocarcinoma diagnosed by pathology as stage II-III (the 8th edition of AJCC staging); (2) no distant metastasis of liver, lung and abdominal cavity in preoperative chest film, abdominal ultrasound and upper abdominal CT; (3) undergoing R0 resection. Case exclusion criteria: (1) receiving preoperative neoadjuvant chemotherapy or radiotherapy; (2) incomplete clinical data; (3) gastric stump cancer.Clinicopathological data of 1231 patients with stage II-III gastric cancer who underwent radical surgery at the Fujian Medical University Union Hospital from January 2010 to August 2014 were retrospectively analyzed. A total of 1035 patients with lymph node metastasis were confirmed after operation, and 196 patients had no lymph node metastasis. According to the postoperative pathologic staging. 416 patients (33.8%) were stage Ⅱ and 815 patients (66.2%) were stage III. Patients were randomly divided into training group (861/1231, 69.9%) and validation group (370/1231, 30.1%) to establish an artificial neural network model (N+-ANN) for the prediction of lymph node metastasis. Firstly, the Logistic univariate analysis method was used to retrospectively analyze the case samples of the training group, screen the variables affecting lymph node metastasis, determine the variable items of the input point of the artificial neural network, and then the multi-layer perceptron (MLP) to train N+-ANN. The input layer of N+-ANN was composed of the variables screened by Logistic univariate analysis. Artificial intelligence analyzed the status of lymph node metastasis according to the input data and compared it with the real value. The accuracy of the model was evaluated by drawing the receiver operating characteristic (ROC) curve and obtaining the area under the curve (AUC). The ability of N+-ANN was evaluated by sensitivity, specificity, positive predictive values, negative predictive values, and AUC values. Results: There were no significant differences in baseline data between the training group and validation group (all P>0.05). Univariate analysis of the training group showed that preoperative platelet to lymphocyte ratio (PLR), preoperative systemic immune inflammation index (SII), tumor size, clinical N (cN) stage were closely related to postoperative lymph node metastasis. The N+-ANN was constructed based on the above variables as the input layer variables. In the training group, the accuracy of N+-ANN for predicting postoperative lymph node metastasis was 88.4% (761/861), the sensitivity was 98.9% (717/725), the specificity was 32.4% (44/136), the positive predictive value was 88.6% (717/809), the negative predictive value was 84.6% (44/52), and the AUC value was 0.748 (95%CI: 0.717-0.776). In the validation group, N+-ANN had a prediction accuracy of 88.4% (327/370) with a sensitivity of 99.7% (309/310), specificity of 30.0% (18/60), positive predictive value of 88.0% (309/351), negative predictive value of 94.7% (18/19), and an AUC of 0.717 (95%CI:0.668-0.763). According to the individualized lymph node metastasis probability output by N+-ANN, the cut-off values of 0-50%, >50%-75%, >75%-90% and >90%-100% were applied and patients were divided into N0 group, N1 group, N2 group and N3 group. The overall prediction accuracy of N+-ANN for pN staging in the training group and the validation group was 53.7% and 54.1% respectively, while the overall prediction accuracy of cN staging for pN staging in the training group and the validation group was 30.1% and 33.2% respectively, indicating that N+-ANN had a better prediction than cN stage. Conclusions: The N+-ANN constructed in this study can accurately predict postoperative lymph node metastasis in patients with stage Ⅱ-Ⅲ gastric cancer. The N+-ANN based on individualized lymph node metastasis probability has better accurate prediction for pN staging as compared to cN staging.


Subject(s)
Artificial Intelligence , Humans , Lymph Nodes/pathology , Lymphatic Metastasis , Neoplasm Staging , Neural Networks, Computer , Prognosis , Retrospective Studies , Stomach Neoplasms/surgery
14.
Article in Chinese | WPRIM | ID: wpr-936040

ABSTRACT

The incidence and mortality rates of gastric cancer are among the top three cancers in China, which poses great threat to people's lives and health. So far, surgery remains to be the cornerstone of treatment for gastric cancer. With the development of laparoscopic surgery, minimally invasive treatment techniques, together with the deepening of clinical researches, as we review the research progress in 2021, the core controversial issues of gastric cancer surgery have been basically addressed. The series of "minimal-innovation" concepts and technologies represented by single-incision/reduced-port laparoscopic surgeries have been further developed; radiomics and artificial intelligence aided prediction have been applied into the forefront of surgical accurate decision-making; targeted and immune-therapy is about to break through the bottleneck of surgical efficacy of gastric cancer. Currently, molecular imaging and targeted tracer guided precision cancer surgery are being explored, which is expected to revolutionize in key links such as real-time in-vivo determination of tumor margin, tracing of metastatic lymph nodes and visualization of nerves. Looking forward into the future, gastric cancer surgery will break through the century-old ceiling of "gross appearance by naked eye" and "traditional extensive experience", and set off a new round of technological revolutions in molecular visualization intelligent precision minimally invasive surgery.


Subject(s)
Artificial Intelligence , Digestive System Surgical Procedures , Gastrectomy , Humans , Laparoscopy , Lymph Node Excision , Minimally Invasive Surgical Procedures , Stomach Neoplasms/surgery
15.
Chinese Journal of Burns ; (6): 481-485, 2022.
Article in Chinese | WPRIM | ID: wpr-936035

ABSTRACT

The accurate diagnosis of burn wound depth is particularly important for evaluating the disease prognosis of burn patients. In the past, the diagnosis of burn wound depth often relied on the subjective judgment of doctors. With the continuous development of diagnostic technology, the methods for judging the depth of burn wound have also been updated. This paper mainly summarizes the research progress in the applications of indocyanine green angiography, laser Doppler imaging, laser speckle contrast imaging, and artificial intelligence in the diagnosis of burn wound depth, and compares the advantages and disadvantages of these techniques, so as to provide ideas for accurate diagnosis of burn wound depth.


Subject(s)
Angiography , Artificial Intelligence , Burns/diagnosis , Humans , Laser-Doppler Flowmetry/methods , Skin , Wound Healing
16.
Chinese Journal of Hepatology ; (12): 443-446, 2022.
Article in Chinese | WPRIM | ID: wpr-935964

ABSTRACT

Artificial intelligence (AI) refers to the use of computer programs to simulate and extend human intelligence, and has application prospects in the diagnosis and treatment of diseases. This review focuses on the research status of the screening and diagnosis of NAFLD and nonalcoholic steatohepatitis using artificial intelligence technology, electronic health record data, multi-omics prediction models, image recognition technology based on liver imaging and pathological biopsy, and new drugs research and development, with a view to provide new ideas for the diagnosis and treatment.


Subject(s)
Artificial Intelligence , Biopsy/methods , Humans , Liver/pathology , Liver Cirrhosis/pathology , Liver Neoplasms/pathology , Non-alcoholic Fatty Liver Disease/pathology
17.
Chinese Journal of Stomatology ; (12): 540-546, 2022.
Article in Chinese | WPRIM | ID: wpr-935899

ABSTRACT

With the advent of the era of big data, artificial intelligence based on machine learning, especially artificial neural network has rapidly developed and applicated in the field of stomatology, owning huge potential in segmentation and labelling of three-dimensional intraoral anatomical features. Automatic segmentation, labelling and diagnosis can assist dentists and technicians to complete heavy and repeat work, change stomatology from subjective perception to objective science, and help to make diagnosis and treatment plan efficiently and accurately. This review briefly summarized related knowledge and development of machine learning and artificial neural network, its application status and existing problems in the field of segmentation and labelling of three-dimensional intraoral anatomical features, and provided an outlook of its future development.


Subject(s)
Artificial Intelligence , Machine Learning , Neural Networks, Computer
18.
Chinese Journal of Surgery ; (12): 498-503, 2022.
Article in Chinese | WPRIM | ID: wpr-935626

ABSTRACT

With the popularization of health screening and the widespread use of low-dose computed tomography, the detection rate of lung nodules has increased year after year. However, the false positive rates testified by surgery of these lung nodules are still high. Therefore, it is vital in clinical practice to avoid overtreatment or undertreatment. But a series of problems on how to make an accurate diagnosis, how to reduce the psychological pressure of patients and follow up with regular imaging, how to clarify the indications for surgery and adopt the most minimally invasive diagnosis and treatment methods, etc. remain unsolved. Over the past decade, the diagnostic techniques for pulmonary nodules have improved significantly, including imaging progress such as the optimization of traditional imaging techniques (CT, MRI) and the emergence of new technologies (radiomics, artificial intelligence). In addition, histological improvements including percutaneous transthoracic needle biopsy, bronchoscopy, and minimally invasive surgical biopsy, etc. have brought more reliable and precise options for characterization of pulmonary nodules.


Subject(s)
Artificial Intelligence , Biopsy, Needle/methods , Bronchoscopy , Humans , Lung Neoplasms/diagnosis , Tomography, X-Ray Computed
19.
Chinese Journal of Surgery ; (12): 1-3, 2022.
Article in Chinese | WPRIM | ID: wpr-935571

ABSTRACT

After more than 20 years of multidisciplinary integration of medical science and technology,as well as research and practice in innovative diagnosis and treatment,digital medicine 4.0 has made a profound and important impact on the development of traditional surgery. To combine traditional surgery with digital medicine 4.0 technology is the direction of surgery development in the future.New technologies represented by digital intelligent navigation surgery have been deeply explored and widely applied in the diagnosis and treatment of many surgical diseases. With the innovative development and application of artificial intelligence,Big Data and mixed reality technology,the surgery will develop in ways similar to aerospace automatic and intelligent navigation,leading to the advent of digital medicine 5.0.


Subject(s)
Artificial Intelligence , Humans , Medicine , Surgery, Computer-Assisted , Technology
20.
Article in Chinese | WPRIM | ID: wpr-935461

ABSTRACT

With the technological progresses and applications of human genome sequencing, bioinformatics analysis and data mining, and molecular pathology and artificial intelligence-assisted pathological diagnosis, the development of clinical medicine is moving towards the era of precision diagnosis and treatment. In the context of this era, the traditional diagnostic pathology is facing unprecedented opportunities and challenges in our history and is striving towards the "next-generation diagnostic pathology" (NGDP). NGDP is based on histomorphology and clinical data, and characterized by the combination of molecular detection and bioinformatics analysis, intelligent sampling and process quality control, intelligent diagnosis and remote consultation, lesion visualization and "non-invasive" pathology as well as other innovative cutting edge interdisciplinary technologies. The NGDP reports will include the results from multi-omics and cross-scale integrated diagnosis for final diagnosis. NGDP will also be applied for predicting disease progression and outcomes, and determining optional therapeutics as well as assessing treatment responses, so that a novel "golden standard" of disease diagnosis can be established. In the near fature, it is necessary to stimulate the innovative vitality of pathology disciplines, accelerate the maturity and application for NGDP, update the theory and technical system of pathology, and perform its important applicable role in the prevention, diagnosis, treatment of diseases so that the futher development of clinical medicine will be promoted and the strategy for maintenance of being healthy in China will be served.


Subject(s)
Artificial Intelligence , China , Computational Biology , Humans , Pathology, Molecular
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