A comparative study of neural network architectures for vital signs monitoring based on the national early warning systems (NEWS).

Objective: The study aims to assess the efficacy of various neural network architectures in predicting the National Early Warning Systems (NEWS) score, using vital signs, to enhance early warning and monitoring in clinical settings. Methods: A comparative evaluation of 29 neural network architectures, including Discriminant Analysis, Support Vector Machines, Logistic Regression, Decision Trees, Neural Networks, and Ensemble methods, was performed. These architectures were assessed based on accuracy, sensitivity, processing speed, model size, and execution time, using synthetically generated data representing 9000 clinical scenarios. Results: The analysis revealed that Linear Discriminant Analysis, narrow and medium Neural Networks, and specific Support Vector Machine (SVM) configurations, particularly Linear SVM, Quadratic SVM, and Coarse Gaussian SVM, achieved 100% accuracy and efficiency in predicting NEWS scores, making them suitable for real-time monitoring. Other architectures exhibited varying performance, with many failing to meet the required accuracy for clinical applications. Conclusion: The study identified Linear Discriminant Analysis and narrow and medium Neural Networks, along with Linear, Quadratic, and Coarse Gaussian SVMs, as optimal for integrating machine learning with NEWS, due to their precision, speed, and suitability for deployment in healthcare environments, particularly in Intensive Care Units.