Hybrid Neural Network Model for Predicting LVO in Ischemic Stroke Patients

Music City Center 

This study aims to develop and validate a novel hybrid neural network (HNN) model that integrates classical statistical methods with ordinary neural networks, combining the strengths of statistical learning and machine learning in sense of structured framework, flexibility and regularization and interpretability.
The proposed HNN model incorporates National Institutes of Health Stroke Scale (NIHSS) item scores, demographic information, medical history, and vascular risk factors to predict LVO. Using both simulated and real-world stroke datasets, we evaluated the model's performance based on sensitivity, specificity, accuracy, and area under the curve (AUC). Comparisons were made against other methods, including logistic regression, Random Forest, Decision Tree, and ordinary neural networks. Results from the study demonstrate that the HNN model consistently outperforms traditional statistical and ML-based approaches. Accuracy of HNN is greater than that of logistic regression or ordinary NN by at least 3%. By leveraging the complementary advantages of statistical and neural network methodologies, the HNN offers a robust and efficient tool for prehospital LVO detection.

Machine Learning

Deep Learning

Hybrid Neural Network

LVO

Stroke

predictive model 

Section on Statistical Computing