DocumentCode :
167283
Title :
Predictive pattern analysis using SOM in medical data sets for medical treatment service
Author :
Young Sung Cho ; Keun Ho Ryu
Author_Institution :
Dept. of Comput. Sci., Chungbuk Nat. Univ., Cheongju, South Korea
fYear :
2014
fDate :
21-24 May 2014
Firstpage :
1
Lastpage :
5
Abstract :
This paper proposes a new method of patterns analysis using SOM in medical data sets for medical treatment service under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to classify disease patterns in the medical historical record to join the information of patient, using SOM neural network with input vectors of different features, disease code, input factors in order to take the medical treatment service in medical data sets, to reduce patients´ search effort to get the information of diagnosis for recovering their health and to improve the rate of accuracy. To verify improved performance, we make experiments with dataset collected in medical center.
Keywords :
diseases; electronic health records; patient diagnosis; patient treatment; pattern classification; self-organising feature maps; ubiquitous computing; SOM neural network; dataset collection; disease code; disease pattern classification; features; health recovering; information diagnosis; input factors; input vectors; medical center; medical data sets; medical historical record; medical treatment service; patient search effort; predictive pattern analysis; rate-of-accuracy; real time accessibility; real time agility; ubiquitous computing environment; Clustering algorithms; Data mining; Diseases; History; Medical diagnostic imaging; Medical treatment; Neural networks; Medical Record; SOMT(Self-Organizing Map); k-Means;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Bioinformatics and Computational Biology, 2014 IEEE Conference on
Conference_Location :
Honolulu, HI
Type :
conf
DOI :
10.1109/CIBCB.2014.6845512
Filename :
6845512
Link To Document :
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