Keywords: Machine Learning/Artificial Intelligence, Modelling, MR Radiology WorkflowInaccurate selection of MRI protocols can impede diagnostics and therapeutic workflows, delay appropriate treatment, increase misdiagnosis likelihoods, and increase healthcare costs. Here we depict a machine-learning (ML) based system to accurately predict abdominal MR protocols, trained on the electronic medical records from 11,251 MR exam orders on 6,882 patients. The best model achieved a cumulative F1-score of 95.6% for top-three most-often-ordered protocols and a top-one F1 score of 78.5%. The proposed system can guide radiologists to appropriate protocol selections quickly, optimize workflows, and improve diagnostic accuracy, thereby by serving to support optimal patient outcomes.
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