Selected techniques for data mining in medicine

被引:214
作者
Lavrac, N [1 ]
机构
[1] Jozef Stefan Inst, Dept Intelligent Syst, Ljubljana 1000, Slovenia
关键词
data mining; machine learning; medical applications;
D O I
10.1016/S0933-3657(98)00062-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:3 / 23
页数:21
相关论文
共 57 条
[31]   THE UTILITY OF BACKGROUND KNOWLEDGE IN LEARNING MEDICAL DIAGNOSTIC RULES [J].
LAVRAC, N ;
DZEROSKI, S ;
PIRNAT, V ;
KRIZMAN, V .
APPLIED ARTIFICIAL INTELLIGENCE, 1993, 7 (03) :273-293
[32]  
LAVRAC N, 1997, INTELLIGENT DATA ANA
[33]  
Lavrac N., 1994, INDUCTIVE LOGIC PROG
[34]  
LESMO L, 1982, APPROXIMATE REASONIN
[35]   LOGIC ENGINEERING IN MEDICINE [J].
LUCAS, PJF .
KNOWLEDGE ENGINEERING REVIEW, 1995, 10 (02) :153-179
[36]  
Michie D., 1994, Technometrics, V37, P459, DOI DOI 10.2307/1269742
[37]  
Mizoguchi F, 1997, KLUWER INT SER ENG C, V414, P227
[38]  
MOZETIC I, UIUCDCSF85949 U ILL
[39]   INVERSE ENTAILMENT AND PROGOL [J].
MUGGLETON, S .
NEW GENERATION COMPUTING, 1995, 13 (3-4) :245-286
[40]  
Muggleton S., 1991, New Generation Computing, V8, P295, DOI 10.1007/BF03037089