{"created":"2025-01-19T01:38:16.747190+00:00","updated":"2025-01-19T09:18:49.419917+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236302","sets":["6504:11678:11689"]},"path":["11689"],"owner":"44499","recid":"236302","title":["SNS投稿から得た文章特徴量を用いたアンサンブル学習によるうつ症状の評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"a382e586-df94-4577-869c-2d55af544117"},"_deposit":{"id":"236302","pid":{"type":"depid","value":"236302","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"SNS投稿から得た文章特徴量を用いたアンサンブル学習によるうつ症状の評価","author_link":["645878","645879"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"SNS投稿から得た文章特徴量を用いたアンサンブル学習によるうつ症状の評価"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2024-03-01","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"横浜国大"},{"subitem_text_value":"横浜国大"}]},"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":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/236302/files/IPSJ-Z86-6W-08.pdf","label":"IPSJ-Z86-6W-08.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-03"}],"format":"application/pdf","filename":"IPSJ-Z86-6W-08.pdf","filesize":[{"value":"334.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"cc5f5405-7c5e-4904-80e2-06a9902142bf","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"長見, 麟太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"濱上, 知樹"}],"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_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"患者数が多く国民に広く関わるものとして5大疾病に指定された精神疾患のうち、最も割合が大きい疾病が双極性障害を含む気分障害である。精神疾患の予防および早期発見に対し人工知能を活用する手法が確立できれば、重症化や自殺の抑制といった社会的要請に応えることができる。そこで本研究では、SNS投稿をエンコードして得られた高次元特徴量にアンサンブル学習を用いてクラスタリングを行い、うつ症状の評価が最小である場合と僅少なリスクを有する場合を誤って判断することが少なくなるような学習モデルを作成した。これによって、文章特徴量を用いるうつ症状アセスメントの有用性を示した。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"940","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"939","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":236302,"links":{}}