{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211731","sets":["1164:5352:10544:10612"]},"path":["10612"],"owner":"44499","recid":"211731","title":["機械学習による急性脳主幹動脈閉塞症の予測精度の向上とアプリケーションの操作性改善"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-06-21"},"_buckets":{"deposit":"d1bb160d-6a32-42a8-b227-dcbfa69778ac"},"_deposit":{"id":"211731","pid":{"type":"depid","value":"211731","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習による急性脳主幹動脈閉塞症の予測精度の向上とアプリケーションの操作性改善","author_link":["538442","538432","538439","538431","538437","538436","538441","538438","538435","538433","538440","538434"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習による急性脳主幹動脈閉塞症の予測精度の向上とアプリケーションの操作性改善"},{"subitem_title":"Improving the prediction accuracy of Emergent Large Vessel Occlusion (ELVO) by machine learning and improving the usability of the application","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"バイオ情報学 (2)","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-06-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京都立産業技術大学院大学産業技術研究科"},{"subitem_text_value":"法政大学理工学部応用情報工学科"},{"subitem_text_value":"独立行政法人国立病院機構災害医療センター"},{"subitem_text_value":"独立行政法人国立病院機構災害医療センター脳神経外科"},{"subitem_text_value":"藤田医科大学医学部脳卒中科"},{"subitem_text_value":"東京都立産業技術大学院大学産業技術研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Industrial Technology, Advanced Institute of Industrial Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Applied Informatics, Faculty of Science and Engineering, Hosei University","subitem_text_language":"en"},{"subitem_text_value":"National Hospital Organization Disaster Medical Center","subitem_text_language":"en"},{"subitem_text_value":"Department of Neurosurgery, National Hospital Organization Disaster Medical Center","subitem_text_language":"en"},{"subitem_text_value":"Department of Comprehensive Strokology, School of Medicine, Fujita Health University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Industrial Technology, Advanced Institute of Industrial Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":44499,"item_file_price":{"attribute_name":"Billing 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孝治"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"林, 昌純"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"清元, 佑紀"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"重田, 恵吾"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松本, 省二"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小山, 裕司"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takaharu, Sato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masazumi, Hayashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuki, Kiyomoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keigo, Shigeta","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shoji, Matsumoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroshi, Koyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究は機械学習技術を用いて急性脳主幹動脈閉塞症の予測精度を向上，ならびにアプリケーションの操作性を改善する試みである．先の研究で我々は機械学習による ELVO 予測アプリケーションを開発し，感度 75%，偽陰性率 25% であった．今回の実装では，1．ELVO 症例数の追加（86 症例），2．拡張期血圧を連続値から 10 刻みへ変更，3．年齢を連続値から離散値へ変更，4．決定木の深さを調整，5．クラスの重みを調整，6．決定木の数の変更を試行し，それぞれ比較した．その結果，1，3，5が有効であることを特定し，感度 93.1%，偽陰性率 6.9% を実現した．また，2 の変更は感度と偽陰性率の向上には寄与しないがアプリケーションの操作性を改善できるため採用した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This study is an attempt to improve the prediction level of Emergent Large Vessel Occlusion (ELVO) by machine learning methods and the usability of the application, called ELVO checker. In our previous result, we developed a machine learning-based ELVO prediction application with a sensitivity of 75% and a false negative rate of 25%. In this paper, we attempted (1) to add ELVO 86 cases, (2) to change the diastolic blood pressure from continuous to 10 increments, (3) to change the age from continuous to discrete, (4) to adjust the maximum depth of decision trees (5) to adjust the class weights, and (6) to change the number of decision trees, and compared these results. As a result, we found the change 1, 3, and 5 were effective and obtained a sensitivity of 93.1% and a false negative rate of 6.9%. The change 2 was not effective, but was adopted since it was expected to improves the usability of the application.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-06-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"28","bibliographicVolumeNumber":"2021-BIO-66"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":211731,"updated":"2025-01-19T17:26:00.889068+00:00","links":{},"created":"2025-01-19T01:12:52.160623+00:00"}