• Matthew I Miller, Agni Orfanoudaki, Michael Cronin, Hanife Saglam, Ivy So Yeon Kim, Oluwafemi Balogun, Maria Tzalidi, Kyriakos Vasilopoulos, Georgia Fanaropoulou, Nina M Fanaropoulou, Jack Kalin, Meghan Hutch, Brenton R Prescott, Benjamin Brush, Emelia J Benjamin, Min Shin, Asim Mian, David M Greer, Stelios M Smirnakis, and Charlene J Ong.
• Department of Neurology, Boston University School of Medicine, 85 E. Concord St., Suite 1116, Boston, MA, 02118, USA.
• Neurocrit Care. 2022 Aug 1; 37 (Suppl 2): 291302291-302.

BackgroundAbstraction of critical data from unstructured radiologic reports using natural language processing (NLP) is a powerful tool to automate the detection of important clinical features and enhance research efforts. We present a set of NLP approaches to identify critical findings in patients with acute ischemic stroke from radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI).MethodsWe trained machine learning classifiers to identify categorical outcomes of edema, midline shift (MLS), hemorrhagic transformation, and parenchymal hematoma, as well as rule-based systems (RBS) to identify intraventricular hemorrhage (IVH) and continuous MLS measurements within CT/MRI reports. Using a derivation cohort of 2289 reports from 550 individuals with acute middle cerebral artery territory ischemic strokes, we externally validated our models on reports from a separate institution as well as from patients with ischemic strokes in any vascular territory.ResultsIn all data sets, a deep neural network with pretrained biomedical word embeddings (BioClinicalBERT) achieved the highest discrimination performance for binary prediction of edema (area under precision recall curve [AUPRC] > 0.94), MLS (AUPRC > 0.98), hemorrhagic conversion (AUPRC > 0.89), and parenchymal hematoma (AUPRC > 0.76). BioClinicalBERT outperformed lasso regression (p < 0.001) for all outcomes except parenchymal hematoma (p = 0.755). Tailored RBS for IVH and continuous MLS outperformed BioClinicalBERT (p < 0.001) and linear regression, respectively (p < 0.001).ConclusionsOur study demonstrates robust performance and external validity of a core NLP tool kit for identifying both categorical and continuous outcomes of ischemic stroke from unstructured radiographic text data. Medically tailored NLP methods have multiple important big data applications, including scalable electronic phenotyping, augmentation of clinical risk prediction models, and facilitation of automatic alert systems in the hospital setting.© 2022. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.

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