A conceptual guide to use and understand Big Data in clinical research

被引:2
|
作者
Andia, Marcelo E. [1 ]
Arrieta, Cristobal [2 ]
Sing Long, Carlos A. [3 ]
机构
[1] Pontificia Univ Catolica Chile, Dept Radiol, Escuela Med, Santiago, Chile
[2] Pontificia Univ Catolica Chile, Ctr Imagenes Biomed, Santiago, Chile
[3] Pontificia Univ Catolica Chile, Inst Ingn Matemat & Computac, Santiago, Chile
来源
REVISTA MEDICA CLINICA LAS CONDES | 2019年 / 30卷 / 01期
关键词
Statistical data analysis; machine learning; artificial intelligence; data mining; ARTIFICIAL-INTELLIGENCE; HEALTH-CARE; FUTURE; CANCER;
D O I
10.1016/j.rmclc.2018.11.003
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Today we find ourselves amidst profound changes in our economy and our society driven by the analysis of massive datasets. Clinical practice and research are poised to be revolutionized by methods that extract useful information from large volumes of medical records that might not be evident when using traditional medical analysis techniques. The number of scientific articles that report successful results when applying these methods of analysis, both in academic and clinical settings, has increased in recent years. Simultaneously, the number of articles in the media warning that medical doctors and radiologists might one day be replaced by these automated methods has also increased. However, how do we evaluate in practice the impact of these methods? This presents a conceptual framework that introduces the main ideas behind Big Data and Data Science and points out the main criteria to be used to assess the potential impact of these techniques in medical research and practice. In addition, we discuss within this framework the results of some of studies that have been reported in the media, and we end by laying out the main challenges that pose the adoption of these methods in practice.
引用
收藏
页码:83 / 94
页数:12
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