Author response for "Early neurological deterioration in cardiogenic cerebral embolism due to nonvalvular atrial fibrillation: Predisposing factors and clinical implications"

2020 ◽  
Author(s):  
Lin Cong ◽  
Weining Ma
Keyword(s):  
Atrial Fibrillation ◽  
Predisposing Factors ◽  
Cerebral Embolism ◽  
Neurological Deterioration ◽  
Author Response ◽  
Clinical Implications ◽  
Nonvalvular Atrial Fibrillation ◽  
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.

Relationship between high-sensitivity C-reactive protein and early neurological deterioration in stroke patients with and without atrial fibrillation

Heart & Lung ◽  
2020 ◽  
Vol 49 (2) ◽  
pp. 193-197
Author(s):  
Zuowei Duan ◽  
Wei Guo ◽  
Tieyu Tang ◽  
Lihong Tao ◽  
Kaizheng Gong ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
High Sensitivity ◽  
Neurological Deterioration ◽  
C Reactive Protein ◽  
Stroke Patients ◽  
Reactive Protein ◽  
Early Neurological Deterioration

Author response for "Effectiveness and Safety of Rivaroxaban and Warfarin for Prevention of Major Adverse Cardiovascular or Limb Events in Nonvalvular Atrial Fibrillation Patients with Type 2 Diabetes"

2019 ◽  
Author(s):  
William L. Baker ◽  
Jan Beyer‐Westendorf ◽  
Thomas J. Bunz ◽  
Daniel Eriksson ◽  
Anna‐Katharina Meinecke ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Type 2 Diabetes ◽  
Author Response ◽  
Nonvalvular Atrial Fibrillation

Evaluation of Risk Factors in Elderly Patients with Cerebral Embolism Due to Nonvalvular Atrial Fibrillation

2011 ◽  
Vol 27 (Supplement) ◽  
pp. PJ1_026
Author(s):  
Mutsumi Miyakoshi ◽  
Hideaki Yoshino ◽  
Kyoko Hoshida ◽  
Yosuke Miwa ◽  
Takehiro Tsukada ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Elderly Patients ◽  
Cerebral Embolism ◽  
Nonvalvular Atrial Fibrillation
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