Application and Exploration of Big Data Mining in Clinical Medicine.
- Author:
Yue ZHANG
;
Shu-Li GUO
;
Li-Na HAN
1
;
Tie-Ling LI
Author Information
- Publication Type:Journal Article
- MeSH: Bayes Theorem; Clinical Medicine; Data Mining; Decision Support Systems, Clinical; Decision Trees; Evidence-Based Medicine; Fuzzy Logic; Humans; Neural Networks (Computer); Pattern Recognition, Automated
- From: Chinese Medical Journal 2016;129(6):731-738
- CountryChina
- Language:English
-
Abstract:
OBJECTIVETo review theories and technologies of big data mining and their application in clinical medicine.
DATA SOURCESLiteratures 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 SELECTIONOriginal articles regarding big data mining theory/technology and big data mining's application in the medical field were selected.
RESULTSThis 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.
CONCLUSIONBig data mining has the potential to play an important role in clinical medicine.
