DocumentCode :
174315
Title :
A directed graph based feature definition for classifying nursing-care texts
Author :
Nii, Manabu ; Takahama, Kazunobu ; Uchinuno, Atsuko ; Sakashita, Reiko
Author_Institution :
Grad. Sch. of Eng., Univ. of Hyogo, Himeji, Japan
fYear :
2014
fDate :
5-8 Oct. 2014
Firstpage :
3691
Lastpage :
3695
Abstract :
In the aging society such as Japan, we feel large importance for improving the quality of nursing-care to keep our quality of life. Development of a computer aided evaluation system for improving the quality of nursing-care is our final goal. In order to evaluate the quality of actual nursing in wide areas in Japan, we have been collecting texts that are written by nurses using our Web based system. A SVM based classification system has been developed to classify such nursing-care texts, and a dependency relation based feature vector definition has also been proposed in our previous researches. When we train the SVM based classification system, pre-classified nursing-care texts by a few nursing-care experts are used as a training data set. Some texts in the training data are similar but classified into different classes. To classify the nursing-care texts with high accuracy, we need to extract numerical features that can express characteristics of the original text. In this paper, we explain some feature vector definitions and propose a directed graph based feature vector definition.
Keywords :
Internet; classification; directed graphs; health care; medical information systems; natural language processing; support vector machines; Japan; SVM based classification system; Web based system; aging society; computer aided evaluation system; dependency relation; directed graph; feature vector definition; nursing-care text; Feature extraction; Indexes; Medical services; Support vector machine classification; Training data; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on
Conference_Location :
San Diego, CA
Type :
conf
DOI :
10.1109/SMC.2014.6974504
Filename :
6974504
Link To Document :
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