{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T23:18:38Z","timestamp":1784675918420,"version":"3.55.0"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T00:00:00Z","timestamp":1509062400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. 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 \u2013 support vector machine, gradient boosting machine, and random forest \u2013 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 models 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 facilitate improvements in the quality and safety of medical imaging service delivery.<\/jats:p>","DOI":"10.1093\/jamia\/ocx125","type":"journal-article","created":{"date-parts":[[2017,10,7]],"date-time":"2017-10-07T11:08:50Z","timestamp":1507374530000},"page":"568-571","source":"Crossref","is-referenced-by-count":76,"title":["Using machine learning for sequence-level automated MRI protocol selection in neuroradiology"],"prefix":"10.1093","volume":"25","author":[{"given":"Andrew D","family":"Brown","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, St Michael\u2019s Hospital, Toronto, ON, Canada"},{"name":"Faculty of Medicine, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas R","family":"Marotta","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, St Michael\u2019s Hospital, 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