著者

Kiyoyuki Chinzei Akinobu Shimizu Kensaku Mori Kanako Harada Hideaki Takeda Makoto Hashizume Mayumi Ishizuka Nobumasa Kato Ryuzo Kawamori Shunei Kyo Kyosuke Nagata Takashi Yamane Ichiro Sakuma Kazuhiko Ohe Mamoru Mitsuishi

出版者

Japanese Society for Medical and Biological Engineering

雑誌

Advanced Biomedical Engineering (ISSN:21875219)

巻号頁・発行日

vol.7, pp.118-123, 2018 (Released:2018-05-24)

参考文献数

3

被引用文献数

30

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AI-based medical and healthcare devices and systems have unique characteristics including 1) plasticity causing changes in system performance through learning, and need of creating new concepts about the timing of learning and assignment of responsibilities for risk management; 2) unpredictability of system behavior in response to unknown inputs due to the black box characteristics precluding deductive output prediction; and 3) need of assuring the characteristics of datasets to be used for learning and evaluation. The Subcommittee on Artificial Intelligence and its Applications in Medical Field of the Science Board, the Pharmaceuticals and Medical Devices Agency (PMDA), Tokyo, Japan, examined “new elements specific to AI” not included in conventional technologies, thereby clarifying the characteristics and risks of AI-based technologies. This paper summarizes the characteristics and clinical positioning of AI medical systems and their applications from the viewpoint of regulatory science, and presents the issues related to the characteristics and reliability of data sets in machine learning.

著者

武田 英明 Hideaki Takeda

雑誌

人工知能 = journal of the Japanese Society for Artificial Intelligence (ISSN:21882266)

巻号頁・発行日

vol.29, no.3, pp.286-291, 2014-05-01

著者

Marie KATSURAI Ikki OHMUKAI Hideaki TAKEDA

出版者

一般社団法人 電子情報通信学会

雑誌

IEICE Transactions on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E99.D, no.4, pp.1010-1018, 2016-04-01 (Released:2016-04-01)

参考文献数

33

被引用文献数

6

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It is crucial to promote interdisciplinary research and recommend collaborators from different research fields via academic database analysis. This paper addresses a problem to characterize researchers' interests with a set of diverse research topics found in a large-scale academic database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert the textual features of a researcher's publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher's interest as a single vector. In experiments conducted on CiNii Articles, which is the largest academic database in Japan, we show that the extracted topics reflect the diversity of the research fields in the database. The experiment results also indicate the applicability of the proposed topic representation to the author disambiguation problem.

著者

飯野 なみ 西村 悟史 西村 拓一 鈴木 美緒 福田 賢一郎 武田 英明 Nami Iino Satoshi Nishimura Takuichi Nishimura Mio Suzuki Ken Fukuda Hideaki Takeda

雑誌

人工知能学会研究会資料

巻号頁・発行日

vol.45, no.5, pp.1-4, 2018-08-03

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This paper describes graphic representation of the process of action based on guitar rendition ontology. We have systematized the knowledge of classical guitar for learning and teaching support, and developed guitar rendition ontology. The ontology defines action process of each rendition by using several properties. However, it is difficult to understand intuitively for players because the ontology presents specific description form. Therefore, in this study, we extracted the knowledge relevant to action from guitar rendition ontology and described action processes by graphical representation.

著者

小出 誠二 武田 英明 Seiji Koide Hideaki Takeda 総合研究大学院大学複合科学研究科情報学専攻 国立情報学研究所:総合研究大学院大学 The Graduate University for Advanced Studies (SOKENDAI) National Institute of Infomatics:The Graduate University for Advanced Studies (SOKENDAI)

出版者

人工知能学会

雑誌

人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence (ISSN:09128085)

巻号頁・発行日

vol.24, no.5, pp.681-690, 2009-09-01

参考文献数

35

著者

Marie KATSURAI Ikki OHMUKAI Hideaki TAKEDA

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E99-D, no.4, pp.1010-1018, 2016-04-01

---

It is crucial to promote interdisciplinary research and recommend collaborators from different research fields via academic database analysis. This paper addresses a problem to characterize researchers' interests with a set of diverse research topics found in a large-scale academic database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert the textual features of a researcher's publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher's interest as a single vector. In experiments conducted on CiNii Articles, which is the largest academic database in Japan, we show that the extracted topics reflect the diversity of the research fields in the database. The experiment results also indicate the applicability of the proposed topic representation to the author disambiguation problem.

著者

武田 英明 Hideaki Takeda 国立情報学研究所:東京大学人工物工学研究センター National Institute of Informatics:RACE The University of Tokyo

出版者

人工知能学会

雑誌

人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence (ISSN:09128085)

巻号頁・発行日

vol.24, no.4, pp.512-518, 2009-07-01

参考文献数

19

被引用文献数

1