Uncovering Discriminative Knowledge-Guided Medical Concepts for Classifying Coronary Artery Disease Notes

Text classification is a challenging task for allocating each document to the correct predefined class. Most of the time, there are irrelevant features which make noise in the learning step and reduce the precision of prediction. Hence, more efficient methods are needed to select or extract meaningf...

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Bibliographic Details
Published inAI 2018: Advances in Artificial Intelligence Vol. 11320; pp. 104 - 110
Main Authors Abdollahi, Mahdi, Gao, Xiaoying, Mei, Yi, Ghosh, Shameek, Li, Jinyan
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2018
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Conceptualization
Coronary artery disease notes
Feature selection
Ontology
Text classification
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Summary:Text classification is a challenging task for allocating each document to the correct predefined class. Most of the time, there are irrelevant features which make noise in the learning step and reduce the precision of prediction. Hence, more efficient methods are needed to select or extract meaningful features to avoid noise and overfitting. In this work, an ontology-guided method utilizing the taxonomical structure of the Unified Medical Language System (UMLS) is proposed. This method extracts concepts of appeared phrases in the documents which relate to diseases or symptoms as features. The efficiency of this method is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set. The obtained experimental results show significant improvement by the proposed ontology-based method on the accuracy of classification.
ISBN:9783030039905
3030039900
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-03991-2_11