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1.
J Med Internet Res ; 22(5): e19540, 2020 05 05.
Artículo en Inglés | MEDLINE | ID: mdl-32353827

RESUMEN

BACKGROUND: Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. OBJECTIVE: We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020. METHODS: The smart contact tracing-based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2. RESULTS: As of February 29, a total of 67 contacts who were tested by reverse transcription-polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020. CONCLUSIONS: Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.


Asunto(s)
Macrodatos , Trazado de Contacto/métodos , Infecciones por Coronavirus/diagnóstico , Infecciones por Coronavirus/prevención & control , Brotes de Enfermedades/prevención & control , Pandemias/prevención & control , Neumonía Viral/diagnóstico , Neumonía Viral/prevención & control , Vigilancia en Salud Pública/métodos , Cuarentena/métodos , Navíos , Betacoronavirus/aislamiento & purificación , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/transmisión , Brotes de Enfermedades/estadística & datos numéricos , Sistemas de Información Geográfica , Humanos , Pandemias/estadística & datos numéricos , Neumonía Viral/epidemiología , Neumonía Viral/transmisión , Estudios Retrospectivos , Distancia Social , Taiwán/epidemiología
2.
Artículo en Inglés | MEDLINE | ID: mdl-32370204

RESUMEN

SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.


Asunto(s)
Inteligencia Artificial , Macrodatos , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/prevención & control , Pandemias/prevención & control , Neumonía Viral/epidemiología , Neumonía Viral/prevención & control , Humanos , Ensayos Clínicos Controlados Aleatorios como Asunto
3.
Curr Sports Med Rep ; 19(4): 157-163, 2020 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-32282462

RESUMEN

Digital transformation is becoming increasingly common in modern life and sports medicine, like many other medical disciplines, it is strongly influenced and impacted by this rapidly changing field. This review aims to give a brief overview of the potential that digital technologies can have for health care providers and patients in the clinical practice of sports medicine. We will focus on mobile applications, wearables, smart devices, intelligent machines, telemedicine, artificial intelligence, big data, system interoperability, virtual reality, augmented reality, exergaming, or social networks. While some technologies are already used in current medical practice, others still have undiscovered potential. Due to the diversity and ever changing nature of this field, we will briefly review multiple areas in an attempt to give readers some general exposure to the landscape instead of a thorough, deep review of one topic. Further research will be necessary to show how digitalization applications could best be used for patient treatments.


Asunto(s)
Medicina Deportiva , Inteligencia Artificial , Macrodatos , Humanos , Aplicaciones Móviles , Telemedicina , Dispositivos Electrónicos Vestibles
6.
Fa Yi Xue Za Zhi ; 36(1): 86-90, 2020 Feb.
Artículo en Inglés, Chino | MEDLINE | ID: mdl-32250085

RESUMEN

Abstract: The estimation of postmortem interval (PMI) is a core issue in forensic practice. A large amount of time-dependent data can be produced in the decomposition process of a body, however, such multidimensional data cannot be comprehensively and effectively analyzed and utilized by any existing conventional PMI estimation method. As a rapidly developing information technology, artificial intelligence (AI) has significant advantages in big data processing, due to it's comprehensiveness, efficiency and automation. Some scholars have already applied it to researches on the estimation of PMI, showing it's significant advantages in terms of accuracy and development prospect. This article reviews the significance, mode and progress of application of AI in PMI estimation and provides some suggestions and prospects for future study.


Asunto(s)
Inteligencia Artificial , Macrodatos , Patologia Forense , Humanos , Cambios Post Mortem , Factores de Tiempo
7.
RECIIS (Online) ; 14(1): 111-125, jan.-mar. 2020.
Artículo en Portugués | LILACS | ID: biblio-1087268

RESUMEN

A informação pública, garantida por lei no Brasil, é base para a geração de conhecimento adaptativo em situações adversas, como a extrema vulnerabilidade socioambiental e seus impactos na saúde humana. O presente artigo avalia a transparência da informação pública nas áreas de saúde humana (com foco no Sistema Único de Saúde ­ SUS), mudanças produtivas (uso do solo) e mudanças climáticas (chuva e temperatura), utilizando dados de 5.570 municípios brasileiros, ao longo dos últimos 20 anos. A experiência da construção de uma base nacional de dados (Data Lake) a partir de informações disponibilizadas em bases públicas (ou público-privadas) ­ DATASUS, MapBiomas, Instituto Nacional de Meteorologia (Inmet) e Hidroweb da Agência Nacional de Águas (ANA) ­ confirmou que, na prática, a acessibilidade da informação pública no Brasil apresenta entraves importantes. Incluímos recomendações sobre como ela pode ser aprimorada para tornar os direitos de acesso à informação uma realidade mais concreta para o cidadão brasileiro.


The transparency of public information, a right that is entitled by law in Brazil, is the basis to generate adaptive knowledge in adverse situations, such as extreme socio-environmental vulnerability and its impacts on human health. This article evaluates the transparency of public information in three areas ­ i) human health, focusing on the Sistema Único de Saúde ­ SUS (Unified Health System); ii) productive changes (land use indicators); and iii) climate changes (rain and temperature indicators) ­ using data from all the 5,570 Brazilian municipalities over the last 20 years. The experience of building a national database (Data Lake) from available information in public (or public-private) databases ­ DATASUS, MapBiomas, Instituto Nacional de Meteorologia ­ Inmet (National Institute of Meteorology), and Hidroweb of the Agência Nacional de Águas ­ ANA (National Water Agency) ­ confirmed that, in practice, the accessibility of public information in Brazil suffers from significant shortcomings. We include some recommendations for and how it could be improved so that the access rights to information becomes a more concrete reality for the Brazilian citizen.


La información pública, garantizada por ley en Brasil, es la base para la generación de conocimiento adaptativo en situaciones adversas, como la extrema vulnerabilidad socioambiental y sus impactos en la salud humana. Este artículo evalúa la transparencia de la información pública en las áreas de salud humana (dirigindo la atención hacia el Sistema Único de Saúde ­ SUS (Sistema Único de Salud), cambios productivos (uso del suelo) y cambios climáticos (lluvia y temperatura), con datos de los 5.570 municipios brasileños, durante los últimos 20 años. La experiencia de la construcción de una base nacional de datos (Data Lake) a partir de informaciones disponibles en bases públicas (o público-privadas) ­ DATASUS, MapBiomas, Instituto Nacional de Meteorología (Inmet) e Hidroweb de la Agência Nacional de Águas ­ ANA (Agencia Nacional de Aguas) ­ confirmó que, en la práctica, la accesibilidad de la información pública en Brasil presenta obstáculos importantes. Incluimos recomendaciones acerca de como la transparencia puede ser perfeccionada para hacer de los derechos de acceso a la información una realidad más concreta para el ciudadano brasileño.


Asunto(s)
Humanos , Cambio Climático , Acceso a la Información/legislación & jurisprudencia , Toma de Decisiones , Medio Ambiente y Salud Pública , Macrodatos , Sistema Único de Salud , Brasil , Información Pública , Vulnerabilidad Social , Sistemas de Información Geográfica , Políticas Públicas de Salud , Sistemas de Información en Salud
8.
Semin Oncol ; 47(1): 56-64, 2020 02.
Artículo en Inglés | MEDLINE | ID: mdl-32229032

RESUMEN

Pediatric cancer is a rare disease with a low annual incidence, which presents a significant challenge in being able to collect enough data to fuel clinical discoveries. Big data registry trials hold promise to advance the study of pediatric cancers by allowing for the combination of traditional randomized controlled trials with the power of larger cohort sizes. The emergence of big data resources and data-sharing initiatives are becoming transformative for pediatric cancer diagnosis and treatment. This review discusses the uses of big data in pediatric cancer, existing pediatric cancer registry initiatives and research, the challenges in harmonizing these data to improve accessibility for study, and building pediatric data commons and other important future endeavors.


Asunto(s)
Macrodatos , Oncología Médica/estadística & datos numéricos , Neoplasias/epidemiología , Pediatría/estadística & datos numéricos , Factores de Edad , Niño , Bases de Datos Factuales , Humanos , Difusión de la Información , Informática Médica/métodos , Oncología Médica/tendencias , Neoplasias/diagnóstico , Neoplasias/etiología , Neoplasias/terapia , Pediatría/tendencias , Vigilancia en Salud Pública , Sistema de Registros , Investigación , Supervivencia
9.
BMC Bioinformatics ; 21(1): 102, 2020 Mar 12.
Artículo en Inglés | MEDLINE | ID: mdl-32164527

RESUMEN

BACKGROUND: All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches. RESULTS: We introduce an alignment-free k-mer based method for detection and quantification of species composition in food and other complex biological matters. It is orders-of-magnitude faster than our previous alignment-based AFS pipeline. In comparison to the established tools CLARK, Kraken2, and Kraken2+Bracken it is superior in terms of false-positive rate and quantification accuracy. Furthermore, the usage of an efficient database partitioning scheme allows for the processing of massive collections of reference genomes with reduced memory requirements on a workstation (AFS-MetaCache) or on a Spark-based compute cluster (MetaCacheSpark). CONCLUSIONS: We present a fast yet accurate screening method for whole genome shotgun sequencing-based biosurveillance applications such as food testing. By relying on a big data approach it can scale efficiently towards large-scale collections of complex eukaryotic and bacterial reference genomes. AFS-MetaCache and MetaCacheSpark are suitable tools for broad-scale metagenomic screening applications. They are available at https://muellan.github.io/metacache/afs.html (C++ version for a workstation) and https://github.com/jmabuin/MetaCacheSpark (Spark version for big data clusters).


Asunto(s)
Macrodatos , Análisis de los Alimentos/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Metagenómica/métodos , Secuenciación Completa del Genoma/métodos , Biovigilancia , Genoma Bacteriano , Metagenoma , Microbiota/genética , Programas Informáticos
11.
J Evid Based Med ; 13(1): 57-69, 2020 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-32086994

RESUMEN

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.


Asunto(s)
Macrodatos , Manejo de Datos , Minería de Datos , Bases de Datos Factuales , Programas Informáticos
12.
PLoS One ; 15(2): e0228728, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32050004

RESUMEN

Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.


Asunto(s)
Macrodatos , Gráficos por Computador , Modelos Teóricos
13.
PLoS One ; 15(2): e0228987, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32097430

RESUMEN

METHODS: Thirty-nine interviews were performed with Swiss and American researchers involved in Big Data research in relevant fields. The interviews were analyzed using thematic coding. RESULTS: No univocal definition of Big Data was found among the respondents and many participants admitted uncertainty towards giving a definition of Big Data. A few participants described Big Data with the traditional "Vs" definition-although they could not agree on the number of Vs. However, most of the researchers preferred a more practical definition, linking it to processes such as data collection and data processing. CONCLUSION: The study identified an overall uncertainty or uneasiness among researchers towards the use of the term Big Data which might derive from the tendency to recognize Big Data as a shifting and evolving cultural phenomenon. Moreover, the currently enacted use of the term as a hyped-up buzzword might further aggravate the conceptual vagueness of Big Data.


Asunto(s)
Macrodatos , Procesamiento Automatizado de Datos , Humanos
14.
Curr Opin Anaesthesiol ; 33(2): 162-169, 2020 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-32022730

RESUMEN

PURPOSE OF REVIEW: The availability of large datasets and computational power has prompted a revolution in Intensive Care. Data represent a great opportunity for clinical practice, benchmarking, and research. Machine learning algorithms can help predict events in a way the human brain can simply not process. This possibility comes with benefits and risks for the clinician, as finding associations does not mean proving causality. RECENT FINDINGS: Current applications of Data Science still focus on data documentation and visualization, and on basic rules to identify critical lab values. Recently, algorithms have been put in place for prediction of outcomes such as length of stay, mortality, and development of complications. These results have begun being implemented for more efficient allocation of resources and in benchmarking processes, to allow identification of successful practices and margins for improvement. In parallel, machine learning models are increasingly being applied in research to expand medical knowledge. SUMMARY: Data have always been part of the work of intensivists, but the current availability has not been completely exploited. The intensive care community has to embrace and guide the data science revolution in order to decline it in favor of patients' care.


Asunto(s)
Macrodatos , Cuidados Críticos/organización & administración , Unidades de Cuidados Intensivos/organización & administración , Benchmarking , Humanos , Aprendizaje Automático
15.
J Korean Med Sci ; 35(7): e57, 2020 Feb 24.
Artículo en Inglés | MEDLINE | ID: mdl-32080989

RESUMEN

BACKGROUND: The big data provided by Health Insurance Review and Assessment (HIRA) contains data from nearly all Korean populations enrolled in the National Health Insurance Service. We aimed to identify the incidence of facial fractures and its trends in Korea using this big data from HIRA. METHODS: We used the Korean Standard Classification of Disease and Cause of Death 6, 7 for diagnosis codes. A total of 582,318 patients were included in the final analysis. All statistical analyses were performed using SAS software and SPSS software. RESULTS: The incidence of facial fractures consistently declined, from 107,695 cases in 2011 to 87,306 cases in 2016. The incidence of facial fractures was the highest in June 2011 (n = 26,423) and lowest in January 2014 (n = 10,282). Nasal bone fractures were the most common, followed by orbit and frontal sinus fractures. The percentage of nasal bone fractures declined, whereas those of orbital fractures increased from 2011 to 2016 (P < 0.001). Among orbital fractures, inferior wall fractures were the most common, followed by medial wall fractures. Among mandibular fractures, angle fractures were the most common, followed by condylar process and symphysis fractures. Although it was difficult to predict the most common type of zygomatic and maxilla fractures, their incidence consistently declined since 2011. CONCLUSION: We observed trends in facial fractures in Korea using big data including information for nearly all nations in Korea. Therefore, it is possible to predict the incidence of facial fractures. This study is meaningful in that it is the first study that investigated the incidence of facial fractures by specific type.


Asunto(s)
Macrodatos , Huesos Faciales , Traumatismos Faciales , Fracturas Mandibulares , Fracturas Orbitales , Interpretación Estadística de Datos , Huesos Faciales/lesiones , Traumatismos Faciales/epidemiología , Humanos , Fracturas Mandibulares/epidemiología , Fracturas Orbitales/epidemiología , República de Corea/epidemiología , Estudios Retrospectivos
20.
Arch. bronconeumol. (Ed. impr.) ; 56(1): 35-41, ene. 2020. graf, tab
Artículo en Inglés | IBECS | ID: ibc-186464

RESUMEN

Sleep is considered an essential part of life and plays a vital role in good health and well-being. Equally important as a balanced diet and adequate exercise, quality and quantity of sleep are essential for maintaining good health and quality of life. Sleep-disordered breathing is one of the most prevalent conditions that compromises the quality and duration of sleep, with obstructive sleep apnea (OSA) being the most prevalent disorder among these conditions. OSA is a chronic and highly prevalent disease that is considered to be a true public health problem. OSA has been associated with increased cardiovascular, neurocognitive, metabolic and overall mortality risks, and its management is a challenge facing the health care system. To establish the main future lines of research in sleep respiratory medicine, the Spanish Sleep Network (SSN) promoted the 1st World Café experts' meeting. The overall vision was established by consensus as "Sleep as promoter of health and the social impact of sleep disturbances". Under this leitmotiv and given that OSA is the most prevalent sleep disorder, five research lines were established to develop a new comprehensive approach for OSA management: (1) an integrated network for the comprehensive management of OSA; (2) the biological impact of OSA on comorbidities with high mortality, namely, cardiovascular and metabolic diseases, neurocognitive diseases and cancer; (3) Big Data Analysis for the identification of OSA phenotypes; (4) personalized medicine in OSA; and (5) OSA in children: current needs and future perspectives


El sueño se considera una parte esencial de la vida y es vital para una buena salud y para el bienestar. De igual importancia que una dieta equilibrada y una adecuada actividad física, la calidad y la cantidad del sueño son esenciales para mantener una buena salud y calidad de vida. Las alteraciones respiratorias del sueño son los trastornos más prevalentes que comprometen la calidad y duración del sueño, siendo el síndrome de la apnea obstructiva del sueño (SAHS) el más frecuente. El SAHS es una enfermedad de elevada prevalencia que se considera un problema de salud pública. Se ha asociado con aumento del riesgo cardiovascular, neurocognitivo, metabólico y especialmente de mortalidad, y su manejo representa un reto para el sistema de salud. Para establecer las principales líneas futuras de investigación en medicina respiratoria del sueño, el Spanish Sleep Network promovió la primera edición del World Cafe experts' meeting. El mensaje principal «El sueño como promotor de la salud y el impacto social de los trastornos del sueño» se estableció por consenso. Bajo este lema y dado que el SAHS es el trastorno del sueño más prevalente, se establecieron cinco líneas de investigación para desarrollar una aproximación completa para el manejo de este síndrome: 1) Una red integrada para el manejo del SAHS; 2) El impacto biológico del SAHS en las comorbilidades con elevada mortalidad como la enfermedad cardiovascular, las enfermedades metabólicas y neurocognitivas y el cáncer; 3) El análisis de grandes bases de datos para la identificación de fenotipos del SAHS; 4) Medicina personalizada en el SAHS, y 5) El SAHS en niños: necesidades actuales y perspectivas futuras


Asunto(s)
Humanos , Atención Integral de Salud/tendencias , Apnea Obstructiva del Sueño/diagnóstico , Apnea Obstructiva del Sueño/terapia , Ventilación con Presión Positiva Intermitente/métodos , Apnea Obstructiva del Sueño/complicaciones , Respiración con Presión Positiva/métodos , Macrodatos , Fenotipo
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