Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization.

Journal: Journal of the American College of Radiology : JACR

Published Date: Jun 1, 2019

Abstract

OBJECTIVE: Radiology is a finite health care resource in high demand at most health centers. However, anticipating fluctuations in demand is a challenge because of the inherent uncertainty in disease prognosis. The aim of this study was to explore the potential of natural language processing (NLP) to predict downstream radiology resource utilization in patients undergoing surveillance for hepatocellular carcinoma (HCC).

Authors

A D Brown

Division of Vascular and Interventional Radiology, Department of Medical Imaging, Toronto General Hospital-University Health Network/University of Toronto, Toronto, Canada. Electronic address: andrew.brown@sloan.mit.edu.
J R Kachura

Division of Vascular and Interventional Radiology, Department of Medical Imaging, Toronto General Hospital-University Health Network/University of Toronto, Toronto, Canada.

Keywords

Aged Area Under Curve Carcinoma, Hepatocellular Databases, Factual Female Health Resources Humans Liver Neoplasms Machine Learning Male Middle Aged Natural Language Processing Ontario Predictive Value of Tests Radiology Department, Hospital Radiology Information Systems Research Report Retrospective Studies ROC Curve Sensitivity and Specificity Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (30833164)

Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization.

Abstract

Authors

Keywords

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