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PLoS One. 2019 Feb 28;14(2):e0212778. doi: 10.1371/journal.pone.0212778. eCollection 2019.

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

Kim C1,2,3, Zhu V2,3, Obeid J2,3, Lenert L2,3,4.

Author information

1
Department of Neurology, Hallym University College of Medicine, Chuncheon, Korea.
2
Medical University of South Carolina, Charleston, South Carolina, United States of America.
3
Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, United States of America.
4
Department of Internal Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America.

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.

MATERIALS AND METHODS:

All brain MRI reports from a single academic institution over a two year period were randomly divided into 2 groups for ML: training (70%) and testing (30%). Using "quanteda" NLP package, all text data were parsed into tokens to create the data frequency matrix. Ten-fold cross-validation was applied for bias correction of the training set. Labeling for AIS was performed manually, identifying clinical notes. We applied binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine for the binary classifiers, and we assessed performance of the algorithms by F1-measure. We also assessed how n-grams or term frequency-inverse document frequency weighting affected the performance of the algorithms.

RESULTS:

Of all 3,204 brain MRI documents, 432 (14.3%) were labeled as AIS. AIS documents were longer in character length than those of non-AIS (median [interquartile range]; 551 [377-681] vs. 309 [164-396]). Of all ML algorithms, single decision tree had the highest F1-measure (93.2) and accuracy (98.0%). Adding bigrams to the ML model improved F1-mesaure of naïve Bayesian classification, but not in others, and term frequency-inverse document frequency weighting to data frequency matrix did not show any additional performance improvements.

CONCLUSIONS:

Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. Single decision tree was the best classifier to identify brain MRI reports with AIS.

Conflict of interest statement

Drs. Kim, Zhu and Obeid have no competing interests. Dr. Lenert is a member of the Board of Directors of the ATCC. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

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