Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Shoya, Wada"'
Autor:
Kento Sugimoto, Shoya Wada, Shozo Konishi, Katsuki Okada, Shirou Manabe, Yasushi Matsumura, Toshihiro Takeda
Publikováno v:
JMIR Medical Informatics, Vol 11, Pp e49041-e49041 (2023)
Abstract BackgroundRadiology reports are usually written in a free-text format, which makes it challenging to reuse the reports. ObjectiveFor secondary use, we developed a 2-stage deep learning system for extracting clinical information and convert
Externí odkaz:
https://doaj.org/article/7a4a641b1640459bb499f1cd2b6f6df0
Autor:
Kento SUGIMOTO, Shoya WADA, Shozo KONISHI, Katsuki OKADA, Keisuke NAKASHIMA, Yasushi MATSUMURA, Toshihiro TAKEDA
Publikováno v:
Studies in Health Technology & Informatics; 2024, Vol. 316, p1795-1799, 5p
Autor:
Kento SUGIMOTO, Shoya WADA, Shozo KONISHI, Katsuki OKADA, Shirou MANABE, Yasushi MATSUMURA, Toshihiro TAKEDA
Publikováno v:
Studies in Health Technology & Informatics; 2023, Vol. 310, p569-573, 5p
Development of the Clinical Specimen Information Management System for Multicenter Clinical Studies.
Autor:
Katsuki OKADA, Kento SUGIMOTO, Shoya WADA, Shozo KONISHI, Shirou MANABE, Yasushi MATSUMURA, Toshihiro TAKEDA
Publikováno v:
Studies in Health Technology & Informatics; 2023, Vol. 310, p119-123, 5p
Autor:
Shoya Wada, Toshihiro Takeda, Katsuki Okada, Shirou Manabe, Shozo Konishi, Jun Kamohara, Yasushi Matsumura
BACKGROUND Pre-training large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirection
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::426134629bf43c3043b109befc6e6669
https://doi.org/10.2196/preprints.40992
https://doi.org/10.2196/preprints.40992
Background: Pre-training large-scale neural language models on raw texts has been shown to make a significant contribution to a strategy for transfer learning in natural language processing (NLP). With the introduction of transformer-based language m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85c4f8adb3d898b11c41a8365c874421
https://doi.org/10.21203/rs.3.rs-103477/v1
https://doi.org/10.21203/rs.3.rs-103477/v1
Autor:
Shozo Konishi, Katsuki Okada, Shoya Wada, Bowen Wang, Toshihiro Takeda, Kento Sugimoto, Shirou Manabe, Jiahao Zhang, Yasushi Matsumura
Publikováno v:
Computer methods and programs in biomedicine. 209
Background and objective In this study, we tried to create a machine-learning method that detects disease lesions from chest X-ray (CXR) images using a data set annotated with extracted CXR reports information. We set the nodule as the target disease
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5e20ec244b61a6851d9117f07f6f0bc
https://pubmed.ncbi.nlm.nih.gov/34418813
https://pubmed.ncbi.nlm.nih.gov/34418813
Autor:
Kento, Sugimoto, Toshihiro, Takeda, Shoya, Wada, Asuka, Yamahata, Shozo, Konishi, Shiro, Manabe, Yasushi, Matsumura
Publikováno v:
Studies in health technology and informatics. 270
Radiology reports include various types of clinical information that are used for patient care. Reports are also expected to have secondary uses (e.g., clinical research and the development of decision support systems). For secondary use, it is neces
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::7096fccf729ed130d6a5e0d6a6a241a8
https://pubmed.ncbi.nlm.nih.gov/32570375
https://pubmed.ncbi.nlm.nih.gov/32570375
Autor:
Shiro Manabe, Asuka Yamahata, Toshihiro Takeda, Takashi Matsunaga, Shozo Konishi, Noriyuki Tomiyama, Kento Sugimoto, Shoya Wada, Jong-Hoon Oh, Katsuyuki Nakanishi, Yasushi Matsumura
Publikováno v:
Journal of biomedical informatics. 116
Extracting clinical terms from free-text format radiology reports is a first important step toward their secondary use. However, there is no general consensus on the kind of terms to be extracted. In this paper, we propose an information model compri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f095ed6dfeb380b796ea34473b5f108e
https://pubmed.ncbi.nlm.nih.gov/33711545
https://pubmed.ncbi.nlm.nih.gov/33711545
Publikováno v:
Medinfo; 2019, p423-427, 5p