Predicting High Imaging Utilization Based On Initial Radiology Reports: A Feasibility Study of Machine Learning
Title:
Predicting High Imaging Utilization Based On Initial Radiology Reports: A Feasibility Study of Machine Learning
Link:
https://www.sciencedirect.com/science/article/pii/S1076633215004237
Abstract:
Rationale and Objectives Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a prediction model to recognize high imaging utilizers based on their initial imaging reports.
Materials and Methods The prediction model uses a machine learning text classification framework. In this study, we used radiology reports from 18,384 patients with at least one abdomen computed tomography study in their imaging record at Stanford Health Care as the training set. We modeled the radiology reports in a vector space and trained a support vector machine classifier for this prediction task. We evaluated our model on a separate test set of 4791 patients. In addition to high prediction accuracy, in our method, we aimed at achieving high specificity to identify patients at high risk for high imaging utilization.
Results Our results (accuracy: 94.0%, sensitivity: 74.4%, specificity: 97.9%, positive predictive value: 87.3%, negative predictive value: 95.1%) show that a prediction model can enable healthcare providers to identify in advance patients who are likely to be high utilizers of imaging services.
Conclusions Machine learning classifiers developed from narrative radiology reports are feasible methods to predict imaging utilization. Such systems can be used to identify high utilizers, inform future image ordering behavior, and encourage judicious use of imaging.
Citation:
Saeed Hassanpour, Curtis P. Langlotz, “Predicting High Imaging Utilization Based On Initial Radiology Reports: A Feasibility Study of Machine Learning”, Academic Radiology, 23(1):84-89, 2016.
