Machine Learning and Non-Linear Dynamics to the Rescue. Assessment and Prediction of Cardiovascular Status for Decision-Support during Cardiac Arrest

      Аннотация: In this book, new methods of feature extraction, feature selection, stochastic data characterization (modeling), variance reduction, and measures for parametric discrimination are proposed. These methods have implications for data mining (knowledge extraction), machine learning (predictive modeling), and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models that form components of this system are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods.

Ключевые слова: machine learning, Model Selection, defibrillation, signal processing, classification, feature selection, Information Theory, regularization, Non-linear Dynamics, Cardiac Arrest, Feature Extraction, Parameter Tuning, Support Vector Machines, Bias-Variance Tradeoff