Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.

Journal: PloS one

PMID: 30818342

Abstract

BACKGROUND AND PURPOSE: This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes.

Authors

Chulho Kim

Department of Neurology, Hallym University College of Medicine, Chuncheon-si, Korea. Electronic address: gumdol52@hallym.or.kr.
Vivienne Zhu

Medical University of South Carolina, Charleston, South Carolina, United States of America.
Jihad Obeid

Medical University of South Carolina, Charleston, South Carolina, United States of America.
Leslie Lenert

Medical University of South Carolina, Charleston, South Carolina, United States of America.

Keywords

Adult Aged Brain Brain Infarction Case-Control Studies Decision Trees Female Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Natural Language Processing Random Allocation Retrospective Studies Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (30818342)

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.

Abstract

Authors

Keywords

External Resources

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