Automatic Classification of Text Documents Presenting Radiology Examinations

被引：0

作者：

Klos, Monika ^{[1
]}

Zylkowski, Jaroslaw ^{[2
]}

Spinczyk, Dominik ^{[1
]}

机构：

[1] Silesian Tech Univ, Fac Biomed Engn, Roosevelta 40, PL-41800 Zabrze, Poland

[2] Med Univ Warsaw, Dept Clin Radiol 2, Banacha 1a, PL-02097 Warsaw, Poland

来源：

INFORMATION TECHNOLOGY IN BIOMEDICINE (ITIB 2018) | 2019年 / 762卷

关键词：

Medical records; Text mining; Text classifiers; Maximum entropy classifiers; Frequent phrase extraction classifiers;

D O I：

10.1007/978-3-319-91211-0_43

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The paper presents the classification of text documents presenting radiology examinations, taking into consideration two groups: cases with aneurysms and those without it. A database containing descriptions of 1284 cases was classified using the maximum entropy algorithm and frequent phrase extraction. It was revealed that the best method was the classifier using the maximum entropy algorithm based on nouns. The best result obtained was 90% of sensitivity and 70% of specificity. The worse diagnostic capacity demonstrates frequent phrase extraction algorithm. The other classifiers turned out to be less effective, than the random ones.

引用

页码：495 / 505

页数：11

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