{"created":"2025-01-19T01:09:27.239795+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00207843","sets":["6164:6165:6902:10404"]},"path":["10404"],"owner":"44499","recid":"207843","title":["集合知定理に基づくクラス判別法の提案と医療分野への応用に関する検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-11-13"},"_buckets":{"deposit":"24a79ee9-457d-44c2-bbf8-6d9c773782b3"},"_deposit":{"id":"207843","pid":{"type":"depid","value":"207843","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"集合知定理に基づくクラス判別法の提案と医療分野への応用に関する検討","author_link":["519630","519631","519634","519632","519633"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"集合知定理に基づくクラス判別法の提案と医療分野への応用に関する検討"},{"subitem_title":"Class Discrimination Method based on Theory of Collective Intelligence and its Application to Medical Field","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"深層学習,人工知能,集合知定理,アンサンブル学習","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2020-11-13","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本大学"},{"subitem_text_value":"日本大学"},{"subitem_text_value":"日本大学"},{"subitem_text_value":"広島大学"},{"subitem_text_value":"広島大学"}]},"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 file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/207843/files/IPSJ-GNWS2020004.pdf","label":"IPSJ-GNWS2020004.pdf"},"date":[{"dateType":"Available","dateValue":"2022-11-13"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-GNWS2020004.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"29"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"9413d048-af0e-4514-8e0c-68cdf3324ebf","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"吉開, 範章"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"栗野, 俊一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"橋口, 徳一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"加澤, 佳奈"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"森山, 美知子"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ノイズを多く含んだデータに対するクラス判別システムに関する検討を行い,時系列変化も考慮して教師データ自体にノイズが含まれる場合の改善法として集合知定理を活用する方法を示す.その応用分野として,保健指導における患者の重症化リスクを予測する AI を試作し,専門家の予測との一致率を評価した.その結果,人の目視で見逃していた高リスク者を特定可能であり,さらに,提案手法により一致率の予測精度の向上が期待できる事を確認した.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"23","bibliographic_titles":[{"bibliographic_title":"ワークショップ2020 (GN Workshop 2020) 論文集"}],"bibliographicPageStart":"19","bibliographicIssueDates":{"bibliographicIssueDate":"2020-11-13","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":207843,"updated":"2025-01-19T17:13:13.079805+00:00","links":{}}