Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 1.111
Filtrar
1.
Rev. bioét. derecho ; (50): 315-331, nov. 2020.
Artigo em Espanhol | IBECS | ID: ibc-191360

RESUMO

La inteligencia artificial y el Big Data se articulan para poder lidiar con diferentes problemas relacionados con el análisis de datos masivos, en particular información de la COVID-19. En el presente artículo se muestran algunos proyectos de investigación relacionados con el aprendizaje profundo, el aprendizaje automático, el Big Data y la ciencia de datos, tendientes a dar soluciones plausibles bien en el monitoreo, detección, diagnóstico y tratamiento de las enfermedades asociadas con el virus. Con esto en mente, se muestra la correspondencia entre las tecnologías disruptivas y la información crítica, creando sinergias que permiten elaborar sistemas más avanzados de estudio y análisis facilitando la obtención de datos relevantes para la toma de decisiones sanitarias


Artificial intelligence and Big Data are articulated to be able to deal with different problems related to the analysis of big data, in particular, information from the COVID-19. In this sense, this article shows some research projects related to deep learning, machine learning, Big Data and data science, aimed to provide plausible solutions in monitoring, detection, diagnosis and treatment of diseases associated with the virus. The correspondence between disruptive technologies and critical information is shown, creating synergies that allow the development of more advanced systems of study and analysis, facilitating the obtaining of relevant data for health decision-making


La Intel·ligència Artificial I el Big Data s'articulen per poder fer front a diferents problemes relacionats amb l'anàlisi de dades massiu, concretament, informació relativa a la COVID-19. En aquest sentit, en el present article es mostren alguns projectes d'investigació relacionats amb l'aprenentatge profund, l'aprenentatge automàtic, el Big Data I la ciència de dades, capaços de donar solucions plausibles en el monitoratge, detecció, diagnòstic I tractament de les malalties associades amb el virus. Amb això en ment, es mostra la correspondència entre les tecnologies disruptives I la informació crítica, creant sinergies que permeten elaborar sistemes més avançats d'estudi I anàlisi facilitant l'obtenció de dades rellevants per a la presa de decisions sanitàries


Assuntos
Humanos , Inteligência Artificial , Big Data , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Pandemias , Tomada de Decisões , Betacoronavirus , Previsões
2.
BMC Public Health ; 20(1): 1707, 2020 Nov 16.
Artigo em Inglês | MEDLINE | ID: mdl-33198699

RESUMO

BACKGROUND: Despite worldwide calls for precautionary measures to combat COVID-19, the public's preventive intention still varies significantly among different regions. Exploring the influencing factors of the public's preventive intention is very important to curtail the spread of COVID-19. Previous studies have found that fear can effectively improve the public's preventive intention, but they ignore the impact of differences in cultural values. The present study examines the combined effect of fear and collectivism on the public's preventive intention towards COVID-19 through the analysis of social media big data. METHODS: The Sina microblog posts of 108,914 active users from Chinese mainland 31 provinces were downloaded. The data was retrieved from January 11 to February 21, 2020. Afterwards, we conducted a province-level analysis of the contents of downloaded posts. Three lexicons were applied to automatically recognise the scores of fear, collectivism, and preventive intention of 31 provinces. After that, a multiple regression model was established to examine the combined effect of fear and collectivism on the public's preventive intention towards COVID-19. The simple slope test and the Johnson-Neyman technique were used to test the interaction of fear and collectivism on preventive intention. RESULTS: The study reveals that: (a) both fear and collectivism can positively predict people's preventive intention and (b) there is an interaction of fear and collectivism on people's preventive intention, where fear and collectivism reduce each other's positive influence on people's preventive intention. CONCLUSION: The promotion of fear on people's preventive intention may be limited and conditional, and values of collectivism can well compensate for the promotion of fear on preventive intention. These results provide scientific inspiration on how to enhance the public's preventive intention towards COVID-19 effectively.


Assuntos
Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/psicologia , Medo/psicologia , Intenção , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/psicologia , Valores Sociais , Big Data , China/epidemiologia , Infecções por Coronavirus/epidemiologia , Humanos , Pneumonia Viral/epidemiologia , Mídias Sociais
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5644-5648, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019257

RESUMO

Critical care units internationally contain medical devices that generate Big Data in the form of high speed physiological data streams. Great opportunities exist for systemic and reliable approaches for the analysis of high speed physiological data for clinical decision support. This paper presents the instantiation of a Big Data analytics based Health Analytics as-a-Service model. The availability results of the deployment of two instances of Artemis Cloud to support two neonatal ICUs (NICUs) in Ontario Canada are presented.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Big Data , Ciência de Dados , Ontário
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5709-5713, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019271

RESUMO

Health product development has been lately tainted by wariness in manufacturers, which has reduced trust in the system. It also affects Digital Health were patients' big data flows generated by numerous sensors are subject to increased security and confidentiality to lower the risks incurred. Our aim is to increase trust in the system again by implementing a dedicated Blockchain solution where data are automatically stored, and where each actor in the development process can access and host them. Blockchain has its downside, such as a subefficient management of big data flows. This study is a first step toward defining a Blockchain solution that will not deteriorate the Quality of Service in this particular context by using the Quality by Design approach. We will mainly focus on the time to consensus attribute which affects both of them. From our experiments' results generated after running screening design and surface response design on a practical Byzantine Fault Tolerance (pBFT) simulator, we find that the transmission time and the message processing time are the most impacting factors.


Assuntos
Segurança Computacional , Confidencialidade , Big Data , Blockchain , Consenso , Humanos
7.
J Med Internet Res ; 22(10): e21081, 2020 10 14.
Artigo em Inglês | MEDLINE | ID: mdl-33027038

RESUMO

BACKGROUND: COVID-19 is the most widely discussed topic worldwide in 2020, and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. OBJECTIVE: In this paper, a data analytics study on the diffusion of COVID-19 in Italy and the Lombardy Region is developed to define a predictive model tailored to forecast the evolution of the diffusion over time. METHODS: Starting with all available official data collected worldwide about the diffusion of COVID-19, we defined a predictive model at the beginning of March 2020 for the Italian country. RESULTS: This paper aims at showing how this predictive model was able to forecast the behavior of the COVID-19 diffusion and how it predicted the total number of positive cases in Italy over time. The predictive model forecasted, for the Italian country, the end of the COVID-19 first wave by the beginning of June. CONCLUSIONS: This paper shows that big data and data analytics can help medical experts and epidemiologists in promptly designing accurate and generalized models to predict the different COVID-19 evolutionary phases in other countries and regions, and for second and third possible epidemic waves.


Assuntos
Betacoronavirus , Big Data , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Simulação por Computador , Infecções por Coronavirus/transmissão , Ciência de Dados , Humanos , Itália/epidemiologia , Pandemias , Pneumonia Viral/transmissão
8.
Stud Health Technol Inform ; 273: 23-37, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087590

RESUMO

The paper describes the concept of the Industry 4.0 and its reflection in health care. Industry 4.0 connects intelligent production concepts with external factors, including those linked with the production and those linked more with human, as for example intelligent homes or social web systems. Communication, data and information play an important role in the whole system. After explaining basic characteristics of the Industry 4.0 concept and its main parts, we show how they can be utilized in the health care sector and what their advantages are. Key technologies and techniques include Internet of Things, big data, artificial intelligence, data integration, robotization, virtual reality, and 3D printing. Finally, we identify the main challenges and research directions. Among the most important ones are interoperability, standardization, reliability, security and privacy, ethical and legal issues.


Assuntos
Inteligência Artificial , Assistência à Saúde , Big Data , Humanos , Indústrias , Reprodutibilidade dos Testes
9.
BMC Bioinformatics ; 21(1): 430, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32998684

RESUMO

BACKGROUND: A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. RESULTS: We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids 'leakage' during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj . CONCLUSIONS: In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.


Assuntos
Big Data , Análise de Dados , Aprendizado de Máquina , Algoritmos , Automação , Humanos
10.
J Med Internet Res ; 22(10): e21980, 2020 10 09.
Artigo em Inglês | MEDLINE | ID: mdl-33001836

RESUMO

BACKGROUND: In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease's rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19. OBJECTIVE: The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations. METHODS: We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China's new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China's response to the epidemic and to provide lessons for other countries' prevention and control of COVID-19. RESULTS: In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus's sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system. CONCLUSIONS: China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.


Assuntos
Big Data , Infecções por Coronavirus/prevenção & controle , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Betacoronavirus , China/epidemiologia , Segurança Computacional , Infecções por Coronavirus/epidemiologia , Coleta de Dados , Humanos , Disseminação de Informação , Armazenamento e Recuperação da Informação , Pneumonia Viral/epidemiologia , Privacidade
11.
PLoS One ; 15(10): e0241171, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33108386

RESUMO

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.


Assuntos
Betacoronavirus , Infecções por Coronavirus/transmissão , Modelos Teóricos , Pandemias , Pneumonia Viral/transmissão , Transportes , Viagem , Big Data , Telefone Celular , China/epidemiologia , Cidades , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Humanos , Aplicativos Móveis , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Fatores de Tempo , Doença Relacionada a Viagens
12.
Nan Fang Yi Ke Da Xue Xue Bao ; 40(10): 1422-1431, 2020 Oct 30.
Artigo em Chinês | MEDLINE | ID: mdl-33118511

RESUMO

OBJECTIVE: To screen the key genes related to the prognosis of lung adenocarcinoma through big data analysis and explore their clinical value and potential mechanism. METHODS: We analyzed GSE18842, GSE27262, and GSE33532 gene expression profile data obtained from the Gene Expression Omnibus (GEO). Bioinformatics methods were used to screen the differentially expressed genes in lung adenocarcinoma tissues and KEGG and GO enrichment analysis was performed, followed by PPI interaction network analysis, module analysis, differential expression analysis, and prognosis analysis. The expressions of MAD2L1 and TTK by immunohistochemistry were verified in 35 non-small cell lung cancer specimens and paired adjacent tissues. RESULTS: We identified a total of 256 genes that showed significant differential expressions in lung adenocarcinoma, including 66 up-regulated and 190 down-regulated genes. Thirty-two up-regulated core genes were screened by functional analysis, and among them 29 were shown to significantly correlate with a poor prognosis of patients with lung adenocarcinoma. All the 29 genes were highly expressed in lung adenocarcinoma tissues compared with normal lung tissues and were mainly enriched in cell cycle pathways. Seven of these key genes were closely related to the spindle assembly checkpoint (SAC) complex and responsible for regulating cell behavior in G2/M phase. We selected SAC-related proteins TTK and MAD2L1 to test their expressions in clinical tumor samples, and detected their overexpression in lung adenocarcinoma tissues as compared with the adjacent tissues. CONCLUSIONS: Seven SAC complex-related genes, including TTK and MAD2L1, are overexpressed in lung adenocarcinoma tissues with close correlation with the prognosis of the patients.


Assuntos
Adenocarcinoma de Pulmão , Proteínas de Ciclo Celular/genética , Neoplasias Pulmonares , Proteínas Mad2/genética , Proteínas Serina-Treonina Quinases/genética , Proteínas Tirosina Quinases/genética , Adenocarcinoma de Pulmão/genética , Big Data , Biologia Computacional , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Humanos , Neoplasias Pulmonares/genética , Pontos de Checagem da Fase M do Ciclo Celular
13.
Artigo em Inglês | MEDLINE | ID: mdl-33096649

RESUMO

The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM2.5 exposure and health risk during COVID-19, and its driving factors have been analyzed using spatiotemporal big data, including Tencent location-based services (LBS) data, place of interest (POI), and PM2.5 site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM2.5 concentration firstly. Then, population exposure and health risks of PM2.5 during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM2.5 pollution, the relationship between PM2.5 concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM2.5 concentrations distributed with a U-shape, i.e., with a decrease followed by an increase from January to December. In terms of spatial distribution, the PM2.5 concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM2.5 in the west is significantly lower than that in the Middle East. Urban gross domestic product (GDP) and urban green area are negatively correlated with PM2.5; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM2.5 pollution. In terms of reducing the health risks and PM2.5 pollution, several pointed suggestions to improve the status has been proposed.


Assuntos
Big Data , Infecções por Coronavirus , Exposição Ambiental/análise , Pandemias , Material Particulado/análise , Pneumonia Viral , Medição de Risco , Betacoronavirus , China/epidemiologia , Humanos , Oriente Médio , Análise Espaço-Temporal
14.
Nat Commun ; 11(1): 4936, 2020 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-33024098

RESUMO

Wearable exercise trackers provide data that encode information on individual running performance. These data hold great potential for enhancing our understanding of the complex interplay between training and performance. Here we demonstrate feasibility of this idea by applying a previously validated mathematical model to real-world running activities of  ≈ 14,000 individuals with ≈ 1.6 million exercise sessions containing duration and distance, with a total distance of ≈ 20 million km. Our model depends on two performance parameters: an aerobic power index and an endurance index. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights into performance: a highly accurate race time prediction and the identification of key parameters such as the lactate threshold, commonly used in exercise physiology. Correlations between performance indices and training volume and intensity are quantified, pointing to an optimal training. Our findings hint at new ways to quantify and predict athletic performance under real-world conditions.


Assuntos
Modelos Teóricos , Corrida/fisiologia , Atletas , Big Data , Exercício Físico/fisiologia , Humanos , Ácido Láctico/metabolismo , Consumo de Oxigênio , Resistência Física/fisiologia , Dispositivos Eletrônicos Vestíveis
15.
RECIIS (Online) ; 14(3): 597-618, jul.-set. 2020. graf, ilus
Artigo em Português | LILACS | ID: biblio-1121781

RESUMO

Este artigo busca responder a alguns dos desafios de sistematização, indexação e divulgação de variados documentos acadêmicos da área de pensamento social no Brasil pela Biblioteca Virtual do Pensamento Social (BVPS). Argumentamos que a importância da discussão sobre preservação digital para a BVPS cumpre dois objetivos: o de disponibilizar documentos digitalizados a um público mais amplo e o de mapear a produção contemporânea da área, com intuito de criar uma memória intelectual. Neste artigo, nos deteremos sobretudo no segundo objetivo, tendo em vista definir bem como disponibilizar ao público da biblioteca os critérios de seleção e organização do acervo. Dentro dos limites do recorte proposto, por meio de redes de acoplamento bibliográfico, cocitação e mapas semânticos, apresentaremos aqui uma análise preliminar da produção de artigos na área de pensamento social no Brasil. A atual pesquisa é fundamental para a definição das próximas etapas de ampliação do conteúdo da biblioteca, notadamente a definição de novos seletores de busca, a integração de novos autores e autoras à seção Intérpretes e a indexação de trabalhos com temáticas e abordagens caras à área de pensamento social no Brasil.


This article seeks to respond to some of the challenges of systematization, indexing and dissemination of various academic documents in the field of social thought in Brazil by the BVPS ­ Biblioteca Virtual do Pensamento Social (Virtual Library of Social Thought). We argue that the importance of the discussion on digital preservation for the BVPS fulfills two objectives: that of making digitized documents available to a wider audience and that of mapping contemporary production in that field in order to create an intellectual memory. In this article, we will focus mainly on the second objective, in order to define as well as make available to the library public the selection and organization criteria of the collection. Within the limits of the proposed clipping, we will present here a preliminary analysis of the production of articles in the field of social thought in Brazil through networks of bibliographic coupling, co-quotation and semantic maps. The current research is fundamental for the definition of the next steps to expand the content of the library, notably the definition of new search options the integration of new authors in the section Interpreters and the indexing of works containing important themes and approaches for the area of social thought in Brazil.


Este artículo busca responder a algunos de los desafíos de la sistematización, indexación y difusión de diferentes tipos de documentos académicos en el campo del pensamiento social en Brasil por la BVPS ­ Biblioteca Virtual do Pensamento Social (Biblioteca Virtual del Pensamiento Social). Argumentamos que la importancia de la discusión sobre la preservación digital para la BVPS cumple dos objetivos: el de hacer que los documentos digitalizados estén disponibles para una audiencia más amplia y el de mapear la producción contemporánea en el área para crear una memoria intelectual. En este artículo, nos centraremos principalmente en el segundo objetivo, para definir como también para poner a la disposición del público de la biblioteca los criterios de selección y organización de la colección. Dentro de los límites del recorte propuesto, presentaremos aquí un análisis preliminar de la producción de artículos en el campo del pensamiento social en Brasil a través de redes de acoplamiento bibliográfico, cocitación y mapas semánticos. La investigación actual es fundamental para la definición de los próximos pasos para expandir el contenido de la biblioteca, en particular la definición de nuevos selectores de búsqueda, la integración de nuevos autores y autoras en la sección Intérpretes y la indexación de trabajos conteniendo temas y enfoques relevantes para el área de pensamiento social en Brasil.


Assuntos
Humanos , Brasil , Armazenamento e Recuperação da Informação , Bibliotecas Digitais , Big Data , Antropologia Cultural , Sociologia , Registros , Gestão da Informação
16.
RECIIS (Online) ; 14(3): 724-733, jul.-set. 2020. ilus
Artigo em Espanhol | LILACS | ID: biblio-1121946

RESUMO

En esta entrevista a Reciis, Miquel Térmens discute la importancia de la preservación digital para crear un sistema de salud que sea bueno no solo para el futuro, pero para el presente. Estamos en una fase de recopilación y almacenamiento de una gran cantidad de datos sobre el nuevo coronavirus para asegurar su rápida utilización, y su preservación a largo plazo es de interés tanto de los gobiernos como de los grupos de investigación que están trabajando a favor de las soluciones. El gran reto de nuestro presente es investigar cómo hacer preservación digital a una nueva escala, incorporando datos de las redes sociales, datos de investigación y Big Data, pero eso solo va a ser posible con normalización y planificación. Miquel Térmens Graells es doctor en Documentación por la Universidad de Barcelona, es profesor titular y decano de la Facultad de Información y Medios Audiovisuales de la misma universidad.


Assuntos
Humanos , Organização e Administração , Sistemas de Saúde , Curadoria de Dados , Big Data , Análise de Dados , Coleta de Dados , Armazenamento e Recuperação da Informação , Acesso à Informação
17.
Zhonghua Liu Xing Bing Xue Za Zhi ; 41(8): 1220-1224, 2020 Aug 10.
Artigo em Chinês | MEDLINE | ID: mdl-32867427

RESUMO

Objective: To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system. Methods: Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients' population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed. Results: Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population (P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without (P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant (P>0.05). Conclusions: The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Betacoronavirus/genética , Betacoronavirus/isolamento & purificação , Big Data , China/epidemiologia , Cidades , Surtos de Doenças , Humanos , Pandemias , Vigilância da População , RNA Viral/genética , RNA Viral/isolamento & purificação , Reação em Cadeia da Polimerase em Tempo Real
19.
Hu Li Za Zhi ; 67(5): 19-25, 2020 Oct.
Artigo em Chinês | MEDLINE | ID: mdl-32978762

RESUMO

People have traditionally associated being 'not ill' with being 'healthy'. This concept has changed due to the improvement of Taiwan's population structure, advances in medical care, and better education. The word 'health' is defined by the World Health Organization as a state of complete physical, mental, and social well-being, and not merely the absence of disease or infirmity. In the future, people in Taiwan must address the challenges of population aging and create a society oriented to the long-term care needs of its citizens. People have different healthcare requirements during the respective stages of healthy, sub-healthy, and disability. Advancing technology has allowed the creation of many healthcare applications such as "health big data" that incorporate Internet of things (IoT) capabilities. Applying artificial intelligence opens many new possibilities and solutions. This article was written to introduce the application of big data techniques in smart healthcare that are appropriate to the three stages of healthy, sub-healthy, and disability.


Assuntos
Inteligência Artificial , Big Data , Tecnologia Biomédica , Assistência à Saúde/organização & administração , Humanos , Taiwan
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA