{"created":"2025-01-19T00:57:48.526217+00:00","updated":"2025-01-20T00:19:29.438039+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00192011","sets":["1164:3980:9387:9574"]},"path":["9574"],"owner":"44499","recid":"192011","title":["Extracting Class-specific Sequential Pattern for Continuous Glucose Monitoring"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-11-08"},"_buckets":{"deposit":"20beff46-cc7e-4705-9388-ffb125cf9d47"},"_deposit":{"id":"192011","pid":{"type":"depid","value":"192011","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Extracting Class-specific Sequential Pattern for Continuous Glucose Monitoring","author_link":["445102","445099","445106","445100","445105","445098","445101","445103","445097","445104"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Extracting Class-specific Sequential Pattern for Continuous Glucose Monitoring"},{"subitem_title":"Extracting Class-specific Sequential Pattern for Continuous Glucose Monitoring","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"MBL","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-11-08","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"IBM Research - Tokyo"},{"subitem_text_value":"IBM Research - Tokyo"},{"subitem_text_value":"Fujita Health University"},{"subitem_text_value":"The Dai-ichi Life Insurance Company, Limited"},{"subitem_text_value":"Fujita Health University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Fujita Health University","subitem_text_language":"en"},{"subitem_text_value":"The Dai-ichi Life Insurance Company, Limited","subitem_text_language":"en"},{"subitem_text_value":"Fujita Health University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/192011/files/IPSJ-ITS18075018.pdf","label":"IPSJ-ITS18075018.pdf"},"date":[{"dateType":"Available","dateValue":"2020-11-08"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ITS18075018.pdf","filesize":[{"value":"1.1 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":"37"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"79196dba-ea5d-41e4-91a2-fd8a8e14bbaa","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masaki, Ono"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takayuki, Katsuki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaki, Makino"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kyoichi, Haida"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsushi, Suzuki"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masaki, Ono","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takayuki, Katsuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaki, Makino","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kyoichi, Haida","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsushi, Suzuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11515904","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":"2188-8965","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Continuous glucose monitoring (CGM) is temporal time-series data that has been available for approximately 10 years thanks to the invention of a device with low measurement error. Understanding the time series variation of glucose helps you treat specific patient groups better by understanding their lifestyles. Therefore, we propose a method of extracting characteristic sequential patterns from given pairs of labels and sequences. First, we apply time-series clustering to transform CGM value sequences into cluster id sequences. Next, we apply sequential pattern mining to extract frequently occurred sequences. Finally, we evaluate each frequent sequence based on their correlation to a specific class. We experimented with two datasets that is manually created and one real CGM dataset to prove our method is effective.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Continuous glucose monitoring (CGM) is temporal time-series data that has been available for approximately 10 years thanks to the invention of a device with low measurement error. Understanding the time series variation of glucose helps you treat specific patient groups better by understanding their lifestyles. Therefore, we propose a method of extracting characteristic sequential patterns from given pairs of labels and sequences. First, we apply time-series clustering to transform CGM value sequences into cluster id sequences. Next, we apply sequential pattern mining to extract frequently occurred sequences. Finally, we evaluate each frequent sequence based on their correlation to a specific class. We experimented with two datasets that is manually created and one real CGM dataset to prove our method is effective.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告高度交通システムとスマートコミュニティ（ITS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-11-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"18","bibliographicVolumeNumber":"2018-ITS-75"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":192011,"links":{}}