Presentation 2018-09-25
Neural network based cause reclassification of incidents at dispensing in pharmacies.
Yoshiki Kobari, Masaomi Kimura,
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Abstract(in Japanese) (See Japanese page)
Abstract(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)
Keyword(in English) Pharmacy incident reports / Word2Vec / Neural network / K-fold cross-validation
Paper # SSS2018-21
Date of Issue 2018-09-18 (SSS)

Conference Information
Committee SSS
Conference Date 2018/9/25(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Masaomi Kimura(Shibaura Inst. of Tech.)
Vice Chair Makoto Ito(Tsukuba Univ.)
Secretary Makoto Ito(NPO RDA)
Assistant Koh Kawashima(Oriental Motor) / Sei Takahashi(Nihon Univ.)

Paper Information
Registration To Technical Committee on Safety
Language JPN
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
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.)
Date 2018-09-25
Paper # SSS2018-21
Volume (vol) vol.118
Number (no) SSS-221
Page pp.pp.9-12(SSS),
#Pages 4
Date of Issue 2018-09-18 (SSS)