Evaluating Report Text Variation and Informativeness: Natural Language Processing of CT Chest Imaging for Pulmonary Embolism.

Journal: Journal of the American College of Radiology : JACR

Published Date: Feb 13, 2018

Abstract

OBJECTIVE: The aim of this study was to quantify the variability of language in free text reports of pulmonary embolus (PE) studies and to gauge the informativeness of free text to predict PE diagnosis using machine learning as proxy for human understanding.

Authors

Marco D Huesch

Department of Radiology, Milton S. Hershey Medical Center, Hershey, Pennsylvania.
Rekha Cherian

Department of Radiology, Milton S. Hershey Medical Center, Hershey, Pennsylvania.
Sam Labib

Department of Radiology, Milton S. Hershey Medical Center, Hershey, Pennsylvania.
Rickhesvar Mahraj

Department of Radiology, Milton S. Hershey Medical Center, Hershey, Pennsylvania. Electronic address: rmahraj@pennstatehealth.psu.edu.

Keywords

Contrast Media Data Mining Documentation Humans Machine Learning Natural Language Processing Pulmonary Embolism Retrospective Studies Software Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (29396123)

Evaluating Report Text Variation and Informativeness: Natural Language Processing of CT Chest Imaging for Pulmonary Embolism.

Abstract

Authors

Keywords

External Resources

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