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- W4319006857 abstract "Background: Stroke patients are at high risk of developing cognitive impairment and dementia. Failure to identify cognitive impairment in time could hasten the progression of dementia and impact the rehabilitation plan. Therefore, a reliable method is needed to determine a stroke survivor’s susceptibility to post-stroke cognitive impairment and dementia (PSCID). Method: We conducted a retrospective cohort study of cryptogenic stroke (CS) patients from January 1, 2017, to February 28, 2022, using EHR data to query for PSCID onset. A machine learning (ML) model was created to forecast the occurrence of PSCID during follow-up. The features used in the model included elements from EHR that were found to be associated with PSCID in other studies, including sociodemographic, medical, and mental morbidities. Logistic Regression, Random Forest (RF) Classifier, and Gradient Boosting are examples of ML algorithms that were used. The final model used RF Classifier due to its superior performance. Results: Of 390 CS patients (62±16 years, 56.4% male) included in the analysis, 110 (28.2%) had documented PSCID in EHR following the initial stroke. We evaluated our model in a repeated (n=100) 10-fold cross validation scheme and used Synthetic Minority Oversampling Technique (SMOTE) to compensate for class imbalance. We identified the most informative ten features using an Extra Tree classifier, which are age, transient ischemic attack, sex, smoking, diabetes, hypertension, atherosclerosis, anemia, and atrial fibrillation. Using these features, our RF classifier reached a performance of 71.2±6.5% accuracy, 70±4.9% precision, 75±12% recall, and 0.777±0.082 AUC. Conclusion: Our model could predict the CS patients at risk for PSCID with reasonable accuracy using only ten features. Future work should involve a larger cohort along with more advanced machine learning algorithms to enhance the prediction performance." @default.
- W4319006857 created "2023-02-03" @default.
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- W4319006857 date "2023-02-01" @default.
- W4319006857 modified "2023-10-18" @default.
- W4319006857 title "Abstract TP189: Identifying Stroke Patients At Risk For Cognitive Impairment And Dementia Using Electronic Health Record Data And Machine Learning" @default.
- W4319006857 doi "https://doi.org/10.1161/str.54.suppl_1.tp189" @default.
- W4319006857 hasPublicationYear "2023" @default.
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