{"_buckets": {"deposit": "71a13495-ba68-4067-a428-cf0826b7ff46"}, "_deposit": {"created_by": 23, "id": "2001328", "owners": [23], "pid": {"revision_id": 0, "type": "depid", "value": "2001328"}, "status": "published"}, "_oai": {"id": "oai:repository.nii.ac.jp:02001328", "sets": ["1697763112205"]}, "author_link": [], "control_number": "2001328", "item_30002_conference34": {"attribute_name": "会議記述", "attribute_value_mlt": [{"subitem_conference_country": "JPN", "subitem_conference_date": {"subitem_conference_date_language": "en", "subitem_conference_end_day": "15", "subitem_conference_end_month": "12", "subitem_conference_end_year": "2023", "subitem_conference_period": "December 12-15, 2023", "subitem_conference_start_day": "12", "subitem_conference_start_month": "12", "subitem_conference_start_year": "2023"}, "subitem_conference_names": [{"subitem_conference_name": "NTCIR-17 Conference", "subitem_conference_name_language": "en"}], "subitem_conference_sequence": "17", "subitem_conference_sponsors": [{"subitem_conference_sponsor": "National Institute of Informatics", "subitem_conference_sponsor_language": "en"}], "subitem_conference_venues": [{"subitem_conference_venue": "National Institute of Informatics", "subitem_conference_venue_language": "en"}]}]}, "item_30002_creator2": {"attribute_name": "作成者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Yuta Nakamura", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Shouhei Hanaoka", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Shuntaro Yada", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Shoko Wakamiya", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Eiji Aramaki", "creatorNameLang": "en"}]}]}, "item_30002_date11": {"attribute_name": "日付", "attribute_value_mlt": [{"subitem_date_issued_datetime": "2023-12-12", "subitem_date_issued_type": "Issued"}]}, "item_30002_description9": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "This paper describes the Radiology Report TNM staging (RR-TNM) subtask as a part of NTCIR-17 Medical Natural Language Processing for Social Media and Clinical Texts (MedNLP-SC) shared task in 2023. This subtask focused on automated lung cancer staging based on radiology reports. We created a dataset of 243 Japanese radiology reports containing no personal health information. A total of three teams with 16 members participated and submitted seven solutions. The best accuracy scores for the T, N, and M categories reached 67%, 80%, and 93%, respectively. 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NTCIR-17 MedNLP-SC Radiology Report Subtask Overview: Dataset and Solutions for Automated Lung Cancer Staging
https://doi.org/10.20736/0002001328
https://doi.org/10.20736/0002001328