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Paper Abstract and Keywords
Presentation 2015-09-11 11:35
Feature Words to Predict Long Post-Operatively Stay in Semi-structured Medical Records
Takanori Yamashita, Haruka Kubo, Yuusuke Adachi, Yoshifumi Wakata, Brendan Flanagan (Kyushu Univ.), Hidehisa Soejima (Kumamoto Hospital), Naoki Nakashima, Sachio Hirokawa (Kyushu Univ.) NLC2015-32
Abstract (in Japanese) (See Japanese page) 
(in English) The hospital information system has expanded, and importance of medical data utilization is increasing. Electronic medical data include structured digital data and unstructured text data. It is expected that analyzing unstructured text data contribute to clinical decision support and improve medical process.
The present paper analyzes the words that appear in semi-structured medical chart to predict the postoperative length of stay. SVM (support vector machine) were applied to extract feature words that characterize the medical charts of patients who stayed longer than the standard hospitalization. Three measures were proposed and the prediction performance for importance of the feature word was evaluated with respect to each measure. We extract the words that related clinical test, and show high frequency of the words in Objective item. Furthermore, we apply to show feature words that characterize the medical charts of long-hospitalization by Decision tree.
Keyword (in Japanese) (See Japanese page) 
(in English) medical chart / POMR / SVM(support vector machine) / Decision Tree / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 222, NLC2015-32, pp. 75-79, Sept. 2015.
Paper # NLC2015-32 
Date of Issue 2015-09-03 (NLC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NLC  
Conference Date 2015-09-10 - 2015-09-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Convention Room AP Shibuya-Dogenzaka (Tokyo) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The Seventh Text Mining Symposium 
Paper Information
Registration To NLC 
Conference Code 2015-09-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feature Words to Predict Long Post-Operatively Stay in Semi-structured Medical Records 
Sub Title (in English)  
Keyword(1) medical chart  
Keyword(2) POMR  
Keyword(3) SVM(support vector machine)  
Keyword(4) Decision Tree  
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Keyword(6)  
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1st Author's Name Takanori Yamashita  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Haruka Kubo  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Yuusuke Adachi  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
4th Author's Name Yoshifumi Wakata  
4th Author's Affiliation Kyushu University (Kyushu Univ.)
5th Author's Name Brendan Flanagan  
5th Author's Affiliation Kyushu University (Kyushu Univ.)
6th Author's Name Hidehisa Soejima  
6th Author's Affiliation Saiseikai Kumamoto Hospital (Kumamoto Hospital)
7th Author's Name Naoki Nakashima  
7th Author's Affiliation Kyushu University (Kyushu Univ.)
8th Author's Name Sachio Hirokawa  
8th Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2015-09-11 11:35:00 
Presentation Time 25 minutes 
Registration for NLC 
Paper # NLC2015-32 
Volume (vol) vol.115 
Number (no) no.222 
Page pp.75-79 
#Pages 5 
Date of Issue 2015-09-03 (NLC) 

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