Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke.

ABSTRACT: Background Conventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language processing has made it feasible to derive meaning from unstructured data. We aimed to test whether using unstructured text in electronic health records can improve the prediction of functional outcome after acute ischemic stroke. Methods and Results Patients hospitalized for acute ischemic stroke were identified from 2 hospital stroke registries (3847 and 2668 patients, respectively). Prediction models developed using the first cohort were externally validated using the second cohort, and vice versa. Free text in the history of present illness and computed tomography reports was used to build machine learning models using natural language processin

SUBMITTER: Sung SF 

PROVIDER: S-EPMC9075227 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature