scholarly journals Produção científica sobre hospitais no contexto da ciência de dados: um estudo a partir da web of science

2021 ◽  
Vol 26 (Especial) ◽  
pp. 1-16
Author(s):  
Natanael Vitor Sobral ◽  
Gillian Leandro de Queiroga Lima ◽  
Ana Sara Pereira de Melo Sobral
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Text Mining ◽  
Data Warehouse ◽  
Data Analytics ◽  
Data Science ◽  
Web Of Science ◽  
Health Technology ◽  
Health Records

Objetivo: realizar análise bibliométrica sobre as aplicações da ciência de dados no âmbito das organizações hospitalares. Método: por meio de pesquisa na base de dados Web of Science, verificou-se a existência de termos relacionados à ciência de dados, tais como “big data”, “data analytics”, “businesss intelligence”, “data mining”, “data warehouse”, “text mining” e “data science", relacionando-os a hospitais. A análise de dados pautou-se na técnica de análise de redes sociais. O período considerado foi de 2015 a 2019. Resultado: “machine learning” e “electronic health records” despontam como assuntos relevantes. As interações mais expressivas refletem a inclinação da informática médica em assuntos relacionados à tomada de decisão, sistemas de informação para hospitais e unidade de cuidados intensivos. Sobre os campos científicos, nota-se a predominância esperada da área de saúde e dos domínios pertencentes ou fronteiriços à tecnologia. No mais, vê-se que a grande variedade de áreas encontradas acusa a natureza multidisciplinar do assunto, inclusive com importante participação da Ciência da Informação (CI). Em relação à geografia do conhecimento, observa-se um razoável grau de descentralização, havendo produções representativas na América do Norte, Europa e Ásia. Quanto aos veículos de publicação, destaque para os Studies in Health Technology and Informatics, que compreendem uma série de publicações. Os dois periódicos mais representativos da lista, integram, respectivamente, os grupos Springer Nature e Elsevier, grandes players do mercado editorial científico. Conclusões: por fim, evidencia-se a multidisciplinaridade existente em torno do assunto estudado e a relevância da tecnologia para o progresso das organizações hospitalares.

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Roberto Salazar-Reyna ◽  
Fernando Gonzalez-Aleu ◽  
Edgar M.A. Granda-Gutierrez ◽  
Jenny Diaz-Ramirez ◽  
Jose Arturo Garza-Reyes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Research Area ◽  
Engineering Systems ◽  
Content Type ◽  
Healthcare Engineering

PurposeThe objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.Design/methodology/approachA systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.FindingsFrom the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.Research limitations/implicationsThe use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.Originality/valueTo the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

scholarly journals Agrupamentos epistemológicos de artigos publicados sobre big data analytics

2017 ◽  
Vol 29 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Patricia Kuzmenko FURLAN ◽  
Fernando José Barbin LAURINDO
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Isi Web Of Science

Resumo A era do big data já é realidade para empresas e indivíduos, e a literatura acadêmica sobre o tema tem crescido rapidamente nos últimos anos. Neste artigo, pretendeu-se identificar quais são os principais nichos e vertentes de publicação sobre o big data analytics. A opção metodológica foi realizar pesquisa bibliométrica na base de dados ISI Web of Science, utilizando-se aquele termo para focar as práticas de gestão de big data. Foi possível identificar cinco grupos distintos dentre os artigos encontrados: evolução do big data; gestão, negócios e estratégia; comportamento humano e aspectos socioculturais; mineração dos dados (data mining) e geração de conhecimento; e Internet das Coisas. Concluiu-se que o tema é emergente e pouco consolidado, apresentando grande variação nos termos empregados, o que influencia nas buscas bibliográficas. Como resultado complementar da pesquisa, foram identificadas as principais palavras-chave empregadas nas publicações sobre big data analytics, o que contribui para as pesquisas bibliográficas de estudos futuros.

Big Data Analysis for Trend Recognition Using Machine Learning Techniques

2020 ◽  
Vol 10 (4) ◽  
pp. 540-550
Author(s):  
Cerene Mariam Abraham ◽  
Mannathazhathu Sudheep Elayidom ◽  
Thankappan Santhanakrishnan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Work ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Machine Learning Techniques ◽  
Derivative Market

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis. Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data. Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

Machine Learning for Data Mining, Data Science and Data Analytics

2021 ◽  
Vol 14 (5) ◽  
pp. 1358-1359
Author(s):  
Vangipuram Radhakrishna ◽  
Gunupudi Rajesh Kumar ◽  
Gali Suresh Reddy ◽  
Dammavalam Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Analytics ◽  
Data Science

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