Accurately Identifying Cerebroarterial Stenosis from Angiography Reports Using Natural Language Processing Approaches.

Lin, Ching-Heng; Hsu, Kai-Cheng; Liang, Chih-Kuang; Lee, Tsong-Hai; Shih, Ching-Sen; Fann, Yang C

Lin, Ching-Heng; Hsu, Kai-Cheng; Liang, Chih-Kuang; Lee, Tsong-Hai; Shih, Ching-Sen; Fann, Yang C.

Afiliação

Lin CH; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan.
Hsu KC; Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan 33305, Taiwan.
Liang CK; Bioinformatics Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
Lee TH; Bioinformatics Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
Shih CS; Department of Medicine, China Medical University, Taichung 40447, Taiwan.
Fann YC; Artificial Intelligence Center for Medical Diagnosis, China Medical University Hospital, Taichung 40402, Taiwan.

Diagnostics (Basel) ; 12(8)2022 Aug 03.

Article em En | MEDLINE | ID: mdl-36010232

Palavras-chave

cerebrovascular diseases; deep learning; intracranial artery stenosis; natural language processing; ruled-based model

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Taiwan

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