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Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

Saeed Hassanpour¹, Curtis P Langlotz¹

¹Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305.

Academic Radiology

|November 3, 2015

Summary

Machine learning accurately predicts high imaging utilization from radiology reports. This helps providers identify patients at risk, promoting appropriate use of medical imaging services.

Keywords:

Imaging utilization natural language processing prediction modeling radiology report narrative

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Area of Science:

Radiology and Medical Imaging
Health Informatics
Machine Learning in Healthcare

Background:

Medical imaging utilization has risen significantly, necessitating methods to manage its use.
Identifying patients with high imaging utilization is crucial for healthcare providers.

Purpose of the Study:

To develop a prediction model for identifying high imaging utilizers based on initial radiology reports.
To achieve high accuracy and specificity in predicting future imaging needs.

Main Methods:

A machine learning text classification framework was employed.
A support vector machine classifier was trained on radiology reports from 18,384 patients.
The model was evaluated on a separate test set of 4,791 patients.

Main Results:

The prediction model achieved high accuracy (94.0%) and specificity (97.9%).
Positive predictive value was 87.3% and negative predictive value was 95.1%.
The model successfully identified patients likely to be high utilizers of imaging services.

Conclusions:

Machine learning classifiers trained on narrative radiology reports are effective for predicting imaging utilization.
These systems can identify high utilizers and inform future image ordering.
The approach supports the judicious use of medical imaging.