Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

Journal: AJR. American journal of roentgenology

Published Date: Apr 1, 2017

Abstract

OBJECTIVE: The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices.

Authors

Saeed Hassanpour

Lia Harrington, Todd MacKenzie, and Saeed Hassanpour, Geisel School of Medicine at Dartmouth College, Hanover; Roberta diFlorio-Alexander, Katherine Trinh, and Arief Suriawinata, Dartmouth-Hitchcock Medical Center, Lebanon, NH.
Curtis P Langlotz

Stanford University, University Medical Line, Stanford, CA, 94305, US.
Timothy J Amrhein

3 Department of Radiology, Duke University Medical Center, Durham, NC.
Nicholas T Befera

3 Department of Radiology, Duke University Medical Center, Durham, NC.
Matthew P Lungren

Keywords

Academic Medical Centers California Data Mining Humans Image Enhancement Knee Machine Learning Magnetic Resonance Imaging Natural Language Processing North Carolina Pattern Recognition, Automated Radiology Department, Hospital Radiology Information Systems Reproducibility of Results Sensitivity and Specificity Support Vector Machine Workload

External Resources

View on PubMed Access via DOI PubMed (28140627)

Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

Abstract

Authors

Keywords

External Resources

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