Predicting Discharge Disposition Following Meningioma Resection Using a Multiinstitutional Natural Language Processing Model

Abstract

Introduction: Machine learning (ML)-based predictive models are increasingly prevalent in neurosurgery, though most require resource-intensive discrete variable collection. Natural language processing (NLP) allows one to extract meaningful information from large quantities of unstructured free text in a relatively simple manner. Here, we create an NLP-based model that utilizes preoperative notes and radiology reports to predict nonhome discharge. We then present a web-based, point-of-care implementation that can be used to make real-time predictions.

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