Abstract
Despite continued evolution in treatment, stroke continues to represent one of the most common and debilitating diseases patients suffer. We created a novel machine-learning natural language processing algorithm to assist in performing outcomes research for patients undergoing treatment for stroke. This method enhanced our ability to accurately determine neurologic outcomes for all urgent stroke interventions. Results demonstrate stroke severity and functional neurologic outcomes for all ischemic stroke patients undergoing (1) urgent carotid endarterectomy (uCEA)/urgent carotid artery stenting (uCAS), (2) tissue plasminogen activator (tPA) alone, (3) mechanical endovascular reperfusion (MER) alone, (4) tPA + MER, and (5) no intervention.
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