scholarly journals Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

2021 ◽  
Vol 429 ◽  
pp. 118754
Author(s):  
Seong Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
O. Kyungmi ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Interpretable Machine Learning ◽  
Early Neurological Deterioration

scholarly journals Interpretable Machine Learning for Early Neurological Deterioration Prediction in Atrial Fibrillation-Related Stroke

2021 ◽  
Author(s):  
Seong Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi O ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multi-center prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.778, 95% CI, 0.726 - 0.830). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.

scholarly journals Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seong-Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi Oh ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

AbstractWe aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.

Abstract TP434: Machine Learning for Prediction of Early Neurological Deterioration in Acute Minor Ischemic Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Sang Min Sung ◽  
Yoon Jung Kang ◽  
Sung Hwan Jang ◽  
Nae Ri Kim ◽  
Suk Min Lee
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Functional Outcome ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Neurological Deterioration ◽  
Minor Stroke ◽  
Minor Ischemic Stroke ◽  
Early Neurological Deterioration

Introduction: A significant portion of patients with acute minor stroke have poor functional outcome due to early neurological deterioration (END). The purpose of this study is to investigate the applicability of machine learning algorithms to predict END in patients with acute minor stroke. Methods: We collected clinical and neuroimaging information from patients with acute minor stroke with NIHHS score of 3 or less. Early neurological deterioration was defined as any worsening of NIHSS score within three days after admission. Poor functional outcome was defined as a modified Rankin Scale score of 2 or more. We also compared clinical and neuroimaging information between END and No END group. Four machine learning algorithms, i.e., Boosted trees, Bootstrap decision forest, Deep learning, and Logistic Regression, are selected and trained by our dataset to predict early neurological deterioration Results: A total of 739 patients were included in this study. Seventy-eight patients (10.6%) had early neurological deterioration. Among 78 patients with END, 61 (78.2%) had poor functional outcomes at 90 days after stroke onset. On multivariate analysis, NIHSS score on admission (P=0.003), hemorrhagic transformation(P=0.010), and stenosis (P=0.014) or occlusion (P=0.004) of a relevant artery were independently associated with END. Compared with four machine learning algorithms, Boosted trees, Deep learning, and Logistic Regression achieved an excellent prediction of END in patients with acute minor stroke (Boosted trees: accuracy = 0.966, F1 score = 0.8 and an area under the curve value = 0.934, Deep learning :0.966, 0.8, 0. 904, and Logistic Regression : 0.966, 0.8, 0.885). Conclusions: This study suggests that machine learning algorithms which integrate clinical and neuroimaging information accurately predict END in patients with acute minor ischemic stroke. Further studies based on an extensive data set are needed to predict END accurately for treatment strategies and better functional outcome.

Sign in / Sign up

Export Citation Format

Share Document