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A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics
- Source :
- BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-10 (2020), BMC Medical Informatics and Decision Making
- Publication Year :
- 2020
- Publisher :
- BMC, 2020.
-
Abstract
- Background 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. Methods We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling. Results The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as “TIA mimic” and 83% of the “TIA” discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%. Conclusion The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.
- Subjects :
- Male
Stroke mimic
Pilot Projects
030204 cardiovascular system & hematology
Logistic regression
Brain Ischemia
0302 clinical medicine
Risk Factors
Medicine
Medical diagnosis
Transient ischemic attack
Aged, 80 and over
biology
Cerebral infarction
Health Policy
Clinical decision support
Predictive analytics
Middle Aged
Classification
Computer Science Applications
Diagnostic error
Stroke
Ischemic Attack, Transient
Feature selection
lcsh:R858-859.7
Female
Research Article
Health Informatics
lcsh:Computer applications to medicine. Medical informatics
Multiclass classification
03 medical and health sciences
Machine learning
parasitic diseases
ABCD2
Humans
cardiovascular diseases
Prospective study
Aged
business.industry
TIA
Pattern recognition
medicine.disease
nervous system diseases
Logistic Models
biology.protein
Artificial intelligence
TIA clinic
business
Classifier (UML)
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 14726947
- Volume :
- 20
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Medical Informatics and Decision Making
- Accession number :
- edsair.doi.dedup.....5e57ecb7311b1354ae4a3b6e4d502812