{"_buckets": {"deposit": "6e01781b-e31f-4faa-9f35-ddd7af8aff9d"}, "_deposit": {"created_by": 3, "id": "7862", "owners": [3], "pid": {"revision_id": 0, "type": "depid", "value": "7862"}, "status": "published"}, "_oai": {"id": "oai:kyutech.repo.nii.ac.jp:00007862", "sets": ["9"]}, "author_link": ["402", "34549", "34546", "34548", "34551", "34547"], "item_21_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2021-04-29", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "9263-11", "bibliographicPageStart": "9263-1", "bibliographicVolumeNumber": "11", "bibliographic_titles": [{"bibliographic_title": "Scientific Reports"}]}]}, "item_21_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P \u003c 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan–Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P \u003c 3 × 10–14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients.", "subitem_description_type": "Abstract"}]}, "item_21_description_60": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"subitem_description": "Journal Article", "subitem_description_type": "Other"}]}, "item_21_publisher_7": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Nature Publishing Group"}]}, "item_21_relation_12": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isIdenticalTo", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1038/s41598-021-88591-z", "subitem_relation_type_select": "DOI"}}]}, "item_21_rights_13": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "Copyright (c) The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/."}]}, "item_21_select_59": {"attribute_name": "査読の有無", "attribute_value_mlt": [{"subitem_select_item": "yes"}]}, "item_21_source_id_8": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "2045-2322", "subitem_source_identifier_type": "ISSN"}]}, "item_21_subject_16": {"attribute_name": "日本十進分類法", "attribute_value_mlt": [{"subitem_subject": "493", "subitem_subject_scheme": "NDC"}]}, "item_21_text_36": {"attribute_name": "著者所属", "attribute_value_mlt": [{"subitem_text_value": "Massachusetts General Hospital and Harvard Medical School"}, {"subitem_text_value": "Massachusetts General Hospital and Harvard Medical School, Kyushu Institute of Technology"}, {"subitem_text_value": "Massachusetts General Hospital and Harvard Medical School"}, {"subitem_text_value": "Massachusetts General Hospital and Harvard Medical School"}, {"subitem_text_value": "Kyushu Institute of Technology"}, {"subitem_text_value": "Massachusetts General Hospital and Harvard Medical School"}]}, "item_21_text_63": {"attribute_name": "連携ID", "attribute_value_mlt": [{"subitem_text_value": "10830"}]}, "item_21_version_type_58": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Näppi,  Janne J."}], "nameIdentifiers": [{"nameIdentifier": "34546", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Uemura,  Tomoki"}], "nameIdentifiers": [{"nameIdentifier": "34547", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Watari,  Chinatsu"}], "nameIdentifiers": [{"nameIdentifier": "34548", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Hironaka,  Toru"}], "nameIdentifiers": [{"nameIdentifier": "34549", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Kamiya, Tohru"}], "nameIdentifiers": [{"nameIdentifier": "402", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "80295005", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://nrid.nii.ac.jp/ja/nrid/1000080295005/"}, {"nameIdentifier": "55739611300", "nameIdentifierScheme": "Scopus著者ID", "nameIdentifierURI": "https://www.scopus.com/authid/detail.uri?authorId=55739611300"}, {"nameIdentifier": "25", "nameIdentifierScheme": "九工大研究者情報", "nameIdentifierURI": "https://hyokadb02.jimu.kyutech.ac.jp/html/25_ja.html"}]}, {"creatorNames": [{"creatorName": "Yoshida,  Hiroyuki"}], "nameIdentifiers": [{"nameIdentifier": "34551", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2023-02-01"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "LaSEINE-2021_001.pdf", "filesize": [{"value": "2.1 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 2100000.0, "url": {"label": "LaSEINE-2021_001.pdf", "url": "https://kyutech.repo.nii.ac.jp/record/7862/files/LaSEINE-2021_001.pdf"}, "version_id": "99cc06da-8d03-4ab3-9c39-ee49adae6426"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19"}]}, "item_type_id": "21", "owner": "3", "path": ["9"], "permalink_uri": "http://hdl.handle.net/10228/00009065", "pubdate": {"attribute_name": "公開日", "attribute_value": "2023-02-01"}, "publish_date": "2023-02-01", "publish_status": "0", "recid": "7862", "relation": {}, "relation_version_is_last": true, "title": ["U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19"], "weko_shared_id": 3}