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

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

Patients with intracranial artery stenosis show high incidence of stroke. Angiography reports contain rich but underutilized information that can enable the detection of cerebrovascular diseases. This study evaluated various natural language processing (NLP) techniques to accurately identify eleven...

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Bibliographic Details
Main Authors: Ching-Heng Lin, Kai-Cheng Hsu, Chih-Kuang Liang, Tsong-Hai Lee, Ching-Sen Shih, Yang C. Fann
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Diagnostics
Subjects:
intracranial artery stenosis
cerebrovascular diseases
natural language processing
ruled-based model
deep learning
Online Access:https://www.mdpi.com/2075-4418/12/8/1882

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