Application and Exploration of Big Data Mining in Clinical Medicine

• Published in: Chinese medical journal Vol. 129; no. 6; pp. 731 - 738
• Main Authors: Zhang, Yue, Guo, Shu-Li, Han, Li-Na, Li, Tie-Ling
• Format: Journal Article
• Language: English
• Published: China Wolters Kluwer - Medknow Publications 20.03.2016; Medknow Publications and Media Pvt. Ltd; Lippincott Williams & Wilkins Ovid Technologies; Department of Cardiovascular Internal Medicine, Nanlou Branch of Chinese People's Liberation Army General Hospital, Beijing 100853, China%State Key Laboratory of Intelligent Control and Decision, School of Automation, Beijing Institute of Technology, Beijing 100081, China%Department of Cadre Physiotherapy, Chinese People's Liberation Army General Hospital, Beijing 100853, China; Medknow Publications & Media Pvt Ltd; Wolters Kluwer
• Subjects: Algorithms; Automation; Bayes Theorem; Big Data; Big Data; Clinical Medicine; Data Mining; Breast cancer; Clinical Medicine; Data Mining; Decision making; Decision Support Systems, Clinical; Decision Trees; Esophagus; Evidence-Based Medicine; Forecasts and trends; Fuzzy Logic; Fuzzy sets; Hospitals; Humans; Internal medicine; Management; Medical prognosis; Medicine; Neural networks; Neural Networks, Computer; Pattern Recognition, Automated; Prostate cancer; PubMed; Review; Set theory; Software; Technology application; 临床医学; 人工神经网络; 医学领域; 应用; 数据挖掘技术; 数据挖掘理论; 粗糙集理论; China; Big Data; Clinical Medicine; Data Mining
• ISSN: 0366-6999; 2542-5641; 2542-5641
• DOI: 10.4103/0366-6999.178019

Objective： To review theories and technologies of big data mining and their application in clinical medicine. Data Sources： Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection： Original articles regarding big data mining theory/technology and big data mining＇s application in the medical field were selected. Results： This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion： Big data mining has the potential to play an important role in clinical medicine.