Machine Learning Prediction of Stroke Mechanism in Embolic Strokes of Undetermined Source.

ABSTRACT:

Background and purpose

One-fifth of ischemic strokes are embolic strokes of undetermined source (ESUS). Their theoretical causes can be classified as cardioembolic versus noncardioembolic. This distinction has important implications, but the categories' proportions are unknown.

Methods

Using data from the Cornell Acute Stroke Academic Registry, we trained a machine-learning algorithm to distinguish cardioembolic versus non-cardioembolic strokes, then applied the algorithm to ESUS cases to determine the predicted proportion with an occult cardioembolic source. A panel of neurologists adjudicated stroke etiologies using standard criteria. We trained a machine learning classifier using data on demographics, comorbidities, vitals, laboratory results, and echocardiograms. An ense

SUBMITTER: Kamel H 

PROVIDER: S-EPMC8034802 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature