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Paper Abstract and Keywords
Presentation 2018-09-25 14:10
Neural network based cause reclassification of incidents at dispensing in pharmacies.
Yoshiki Kobari (SIT), Masaomi Kimura (Shibaura Inst. of Tech.) SSS2018-21
Abstract (in Japanese) (See Japanese page) 
(in English) Incidents in pharmacies are the cases whose causes are common with medical accidents. Most of the root cause of incidents at dispensing in pharmacies has been reported as confirmation failure. Confirmation failure is not a cause to cause incidents, and it cannot be an essential cause. Because of this, the purpose of this study is to propose a new root cause model according to human action classes, and to show applicability to assistance for pharmacists to identify root causes by using a neural network.
Keyword (in Japanese) (See Japanese page) 
(in English) Pharmacy incident reports / Word2Vec / Neural network / K-fold cross-validation / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 221, SSS2018-21, pp. 9-12, Sept. 2018.
Paper # SSS2018-21 
Date of Issue 2018-09-18 (SSS) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 SSS  
Conference Date 2018-09-25 - 2018-09-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To SSS 
Conference Code 2018-09-SSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Neural network based cause reclassification of incidents at dispensing in pharmacies. 
Sub Title (in English)  
Keyword(1) Pharmacy incident reports  
Keyword(2) Word2Vec  
Keyword(3) Neural network  
Keyword(4) K-fold cross-validation  
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1st Author's Name Yoshiki Kobari  
1st Author's Affiliation Shibaura Institute of Technology (SIT)
2nd Author's Name Masaomi Kimura  
2nd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
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Speaker Author-1 
Date Time 2018-09-25 14:10:00 
Presentation Time 35 minutes 
Registration for SSS 
Paper # SSS2018-21 
Volume (vol) vol.118 
Number (no) no.221 
Page pp.9-12 
#Pages
Date of Issue 2018-09-18 (SSS) 


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