Identification of patients with carotid stenosis using natural language processing.

Journal: European radiology

PMID: 32103365

Abstract

PURPOSE: The highly structured nature of medical reports makes them feasible for automated large-scale patient identification. This study aimed to develop a natural language processing (NLP) model to retrospectively retrieve patients with presence and history of carotid stenosis (CS) using their ultrasound reports.

Authors

Xiao Wu
Yuzhe Zhao

Department of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
Dragomir Radev

Department of Computer Science, Yale University, New Haven, CT, USA.
Ajay Malhotra

Department of Radiology and Biomedical Imaging, Yale University School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT, 06520-8042, USA. ajay.malhotra@yale.edu.

Keywords

Carotid Stenosis Humans Natural Language Processing Neural Networks, Computer Retrospective Studies Sensitivity and Specificity Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (32103365)

Identification of patients with carotid stenosis using natural language processing.

Abstract

Authors

Keywords

External Resources

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