The application of machine learning to evaluate the adequacy of information in radiology orders

Abstract

Background: Adequate clinical information provided with radiology orders is important for an accurate interpretation of imaging studies. Nonetheless, high percentage of radiology orders lack adequate information. Assessment of the adequacy of the information associated with radiology orders could be achieved manually. However, manual assessment is costly and inefficient. Novel approaches using machine learning and text mining to assess the adequacy of radiology order information could reduce the costs and improve efficiency. We aimed to test the application of machine learning algorithms to identify radiology orders with adequate/inadequate information. Methods: We extracted 1,967 electronic chest computed tomography (CT) orders at an academic tertiary hospital during January 2014, and manually classified them into containing adequate or inadequate information based on the American College of Radiology guidelines. We used text mining (text parsing and vectorization) and machine learning (Naïve Bayes, Support Vector Machines and Decision Tree classifiers) to automate order adequacy classification, and evaluated the system performance against the manual review. Results: Surprisingly, only 30.6% of orders had adequate information when evaluated manually. Non-resident physicians provided the least number of adequate order information (26.7%). Classifiers achieved high classification accuracy. Naïve Bayes classifier performed slightly better overall (Accuracy= .9) than Support Vector Machines (Accuracy= .89) and Decision Trees (J48, Accuracy= .85). Conclusions: High percentage of orders lack adequate information in chest CT. Machine learning classifiers could be utilized to assess the adequacy of radiology order information.

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