著者

中 雄介 恩田 光子 山根 有香子 川口 祐司 中野 翔太 荒川 行生

出版者

一般社団法人日本医薬品情報学会

雑誌

医薬品情報学 (ISSN:13451464)

巻号頁・発行日

vol.18, no.2, pp.81-86, 2016 (Released:2016-09-27)

参考文献数

14

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Objective: The subjects of this study were consumers with cold-like symptoms who visited drugstores to purchase OTC drugs.  The purpose was to elucidate the factors that influence the intention of these consumers to consult pharmacists or sellers.Design: Analytic observational studyMethod: We conducted a survey of consumers who visited pharmacies or drugstores for cold-like symptoms.  Pharmacists and registered sellers (hereafter “pharmacists or sellers”) utilized tools to serve them, entering details in customer records.  We handed postcards to these consumers asking them to respond to questions about the prognosis and the degree of satisfaction about the service they had received.  We then used the customer records and follow-up results to perform linear regression analysis with “I would like to consult the pharmacist or seller again” (hereafter “desire for consultation”) as the dependent variable, and the usefulness of the advice and degree of satisfaction about the explanation and service as the independent variables.Results: We analyzed the data of 81 consumers for whom we were able to match the customer records and postcards.  The linear regression analysis indicated that “the usefulness of the advice (coefficient of standardization: 0.73)” affected the desire for consultation most, followed by “the degree of satisfaction about the service (coefficient of standardization: 0.24).Conclusion: We verified that, in self-medication assistance, advice that lets consumers feel the consultation was actually “helpful” by focusing on individual needs, and good customer service were necessary to increase the desire for consultation with pharmacists or sellers, and to encourage actual consultation.

著者

劉 海龍 谷口 忠大 高野 敏明 竹中 一仁 坂東 誉司 田中 雄介

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.28, 2014

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車両を流れる信号は年々増加しており,それらの時系列信号を人間が直感的に理解するのは困難である.本稿では Deep Sparse Autoencoder を用いて車両の運転挙動データを表現する低次元特徴量を抽出することで,運転状況の可視化を試みる.ここでは 100 次元の運転挙動データから 3 次元の特徴量を抽出し,RGB の色空間に対応させて地図上に可視化する手法を提案する.