Using machine learning for sequence-level automated MRI protocol selection in neuroradiology

We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models – support vector machine, gradient boosting machine, and random forest – to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 mo dels in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may fac ilitate improvements in the quality and safety of medical imaging service delivery.
Source: Journal of the American Medical Informatics Association - Category: Information Technology Source Type: research