Early Prediction of Functional Outcomes After Acute Ischemic Stroke Using Unstructured Clinical Text: Retrospective Cohort Study.

ABSTRACT:

Background

Several prognostic scores have been proposed to predict functional outcomes after an acute ischemic stroke (AIS). Most of these scores are based on structured information and have been used to develop prediction models via the logistic regression method. With the increased use of electronic health records and the progress in computational power, data-driven predictive modeling by using machine learning techniques is gaining popularity in clinical decision-making.

Objective

We aimed to investigate whether machine learning models created by using unstructured text could improve the prediction of functional outcomes at an early stage after AIS.

Methods

We identified all consecutive patients who were hospitalized for the first time for AIS from October 2007 to

SUBMITTER: Sung SF 

PROVIDER: S-EPMC8895286 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature