Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.

ABSTRACT:

Purpose

The aims of this study were to assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language processing (NLP) system.

Methods

This HIPAA-compliant, institutional review board-approved study was performed at an academic medical center generating >500,000 radiology reports annually. One thousand randomly selected ultrasound, radiography, CT, and MRI reports generated in 2016 were manually reviewed and annotated for follow-up recommendations. TML (support vector machines, random forest, logistic regression) and DL (recurrent neural nets) algorithms were constructed and trained on 850 reports (training data), with subsequent

SUBMITTER: Carrodeguas E 

PROVIDER: S-EPMC7534384 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature