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| Presentation | 2018-12-15 10:25 Prediction of side effects by deep learning using drug side effect database (JADER) Hiroki Matsui, Sumio Matsuno, Ryo Onoda, Naoki Ohboshi (Kindai Univ.)  NC2018-28 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Drug use Grasping the occurrence trend of adverse events as an application of safety information after marketing contributes to the early detection and adequate response of adverse events and is important in proper use of pharmaceutical products. Also, using these data, it is possible to grasp the tendency of adverse events to develop. Have great value in the decision of a doctor who chooses a medicine. Therefore, in this study, we predict adverse events that could occur by machine learning using medicine side effect database (JADER) published by Independent Administrative Agency Pharmaceuticals and Medical Devices Agency (PMDA) and analyzed the results. Predicting possible adverse events from these data as multi-class classification problems, we could predict with an average of about 81% recall. In addition, when we scrutinized the remaining 19% of unexpected results, there were many cases where many medications were administered, and the possibility that it was an unknown adverse event due to polypharmacy could be considered. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | JADER / Machine Learning / DNN / Side Effect / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 367, NC2018-28, pp. 1-4, Dec. 2018. | |
| Paper # | NC2018-28 | |
| Date of Issue | 2018-12-08 (NC) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and 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) | |
| Download PDF | NC2018-28 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2018-12-15 - 2018-12-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagoya Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2018-12-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Prediction of side effects by deep learning using drug side effect database (JADER) |
| Sub Title (in English) | |
| Keyword(1) | JADER |
| Keyword(2) | Machine Learning |
| Keyword(3) | DNN |
| Keyword(4) | Side Effect |
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| 1st Author's Name | Hiroki Matsui |
| 1st Author's Affiliation | Kindai University (Kindai Univ.) |
| 2nd Author's Name | Sumio Matsuno |
| 2nd Author's Affiliation | Kindai University (Kindai Univ.) |
| 3rd Author's Name | Ryo Onoda |
| 3rd Author's Affiliation | Kindai University (Kindai Univ.) |
| 4th Author's Name | Naoki Ohboshi |
| 4th Author's Affiliation | Kindai University (Kindai Univ.) |
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| Speaker | Author-1 |
| Date Time | 2018-12-15 10:25:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2018-28 |
| Volume (vol) | vol.118 |
| Number (no) | no.367 |
| Page | pp.1-4 |
| #Pages | 4 |
| Date of Issue | 2018-12-08 (NC) |
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