摘要
卒中是一种高发病率、高死亡率、高致残率的疾病,严重威胁人类健康,降低生存质量。国内外卒中数据库及医院信息系统提供大量卒中患者临床及随访资料。数据挖掘作为智能时代的新技术,能够更高效地分析大量临床资料,进行卒中病因、诊断、疗效及预后等方面的研究。本文将分两部分介绍数据挖掘技术在卒中相关研究中的应用:(1)数据挖掘技术分析临床资料的基本步骤,此部分重点探讨数据分析过程一般步骤以及常用数据挖掘预测模型;(2)数据挖掘技术在卒中相关研究中的应用举例,此部分总结目前国内外基于数据挖掘技术的卒中相关研究,列出6种可应用数据挖掘进行的研究主题。
Stroke is a cerebrovascular disease with high incidence, high mortality rate and high disability rate, a serious threat to human health, which reduces the quality of life. Domestic and foreign stroke database and hospital information system have provided a large number of clinical and follow-up data of stroke patients. Data mining method as a new technique of the intelligentera, can be used to analyze a large number of clinical data in the aspects of the cause, diagnosis, and prognosis of stroke. This paper introduces the application of data mining technology in stroke related research in two parts. Firstly, the basic steps of the data mining technology aim to analyze clinical data. This part focuses on the general steps of the data analysis process and commonly used data mining prediction models. Secondly, the application of data mining technology in the stroke related research. This part summarizes the current domestic and foreign stroke research based on data mining technology and demonstrates six kinds of stroke related research themes that can be analyzed by data mining technique.
作者
娜迪热.艾孜热提艾力
刘煜敏
Nadire·Aiziretiaili;LIU Yu-Min(Department of Neurology,Kashi Prefecture First People＇s Hospital,Kashi Prefecture 844000,China;Department of Neurology,Zhongnan Hospital of Wuhan University,Wuhan 430072,China)
出处
《中国卒中杂志》
2018年第8期881-886,共6页
Chinese Journal of Stroke
基金
国家自然科学基金(81371273)
关键词
卒中
数据挖掘
相关因素
预测模型
Stroke
Data Mining
Relevant Factor
Predictive Model
