A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics

Authors

Alia C. Stanciu, Bucknell University
Mihai Banciu, Bucknell UniversityFollow
Alireza Sadighi, Geisinger Medical Center
Kyle A. Marshall, Geisinger Medical Center
Neil Holland, Geisinger Medical Center
Vida Abedi, Geisinger Medical Center
Ramin Zand, Geisinger Medical Center

Publication Date

6-18-2020

Description

Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke.

Journal

BMC Medical Informatics and Decision Making

Volume

20

Issue

1

First Page

112

Last Page

112

Department

College of Management

Second Department

College of Management

Link to Published Version

https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01154-6

DOI

10.1186/s12911-020-01154-6

Recommended Citation

Stanciu, Alia C.; Banciu, Mihai; Sadighi, Alireza; Marshall, Kyle A.; Holland, Neil; Abedi, Vida; and Zand, Ramin. "A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics." (2020) : 112-112.