The Potential of Big Data Research in HealthCare for Medical Doctors' Learning.
The Potential of Big Data Research in HealthCare for Medical Doctors' Learning.
复制标题
医疗保健大数据研究对医生学习的潜力。
DOI：
10.1007/s10916-020-01691-7
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发表时间：
2021-01-07
影响因子：
5.3
通讯作者：
Soliman M
中科院分区：
文献类型：
--
作者：
Au-Yong-Oliveira M;Pesqueira A;Sousa MJ;Dal Mas F;Soliman M
本文的主要目标是确定一个模型提案的主要维度，以增加大数据研究在医疗保健医生（MD）学习中的潜力，这似乎是继续医学教育和学习中的一个主要问题。本文采用了系统的文献综述的主要科学数据库（PubMed和谷歌学术），使用VOSviewer软件工具，使科学景观的可视化。该分析包括共同作者数据分析以及术语和关键词的共现。研究结果导致了所提出的学习模型的构建，该模型包括医学博士学习的四个健康大数据关键领域：1）数据转换与通过医疗系统发生的学习有关; 2）健康智能包括基于预测和预测过程的健康创新学习; 3）数据利用关于患者信息的学习;（4）学习过程与临床决策有关，侧重于疾病诊断和改进治疗方法。从科学数据库中收集的实用模型可以促进学习过程并彻底改变医疗行业，因为它们存储了最新的知识和创新研究。
The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors’ (MDs’) learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs’ learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research.
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影响因子：
3.7
作者：
Carra, Giorgia;Salluh, Jorge I. F.;Meyfroidt, Geert
通讯作者：
Meyfroidt, Geert
影响因子：
--
作者：
Baro E;Degoul S;Beuscart R;Chazard E
通讯作者：
Chazard E
DOI：
10.1016/j.techfore.2013.07.001
发表时间：
2014-02-01
影响因子：
12
作者：
Derbyshire, James;Wright, George
通讯作者：
Wright, George
影响因子：
--
作者：
Guan, Jian
通讯作者：
Guan, Jian
DOI：
10.1073/pnas.0610941104
发表时间：
2007-05-01
影响因子：
11.1
作者：
Hatchett, Richard J.;Mecher, Carter E.;Lipsitch, Marc
通讯作者：
Lipsitch, Marc