Description
Preface.- Foundations of machine learning-based clinical prediction modeling – Part I: Introduction and general principles.- Foundations of machine learning-based clinical prediction modeling – Part II: Generalization and Overfitting.- Foundations of machine learning-based clinical prediction modeling – Part III: Evaluation and other points of significance.- Foundations of machine learning-based clinical prediction modeling – Part IV: A practical approach to binary classification problems.- Foundations of machine learning-based clinical prediction modeling – Part V: A practical approach to regression problems.- Supervised and unsupervised learning / clustering.- Introduction to Bayesian Modeling.- Introduction to Deep Learning.- Overview of algorithms for machine-learning based clinical prediction modelling.- Foundations of feature selection in clinical prediction modelling.- Dimensionality reduction: Foundations and applications in clinical neuroscience.- Machine learning-based survival modeling: Foundations and Applications.- Making clinical prediction models available: A brief introduction.- Machine Learning-based Clustering Analysis: Foundational Concepts, Methods, and Applications.- Introduction to Machine Learning in Neuroimaging.- Overview of machine learning algorithms in imaging.- Foundations of classification modeling based on neuroimaging.- Foundations of lesion-symptom mapping using machine learning.- Foundations of Machine Learning-Based Segmentation in Cranial Imaging.- Foundations of lesion detection using machine learning in clinical neuroimaging.- Foundations of multiparametric brain tumor imaging characterization.- Radiomics in clinical neuroscience – Overview.- Radiomic feature extraction: Methodological Foundations.- Complexity and interpretability in machine vision.- Foundations of intraoperative anatomical recognition using machine vision.- Machine Vision Foundations.- Natural Language Processing: Foundations and Applications in Clinical Neuroscience.- Foundations of Time Series Analysis.- Overview of algorithms for natural language processing and time series analysis.- History of machine learning in neurosurgery.- The AI doctor – considerations for AI-based medicine.- Ethics of Machine Learning-Based Predictive Analytics.- Predictive analytics in clinical practice: Pro and contra.- Review of machine vision applications in neuroophtalmology.- Prediction Model.- Prediction Model.- Prediction Model.- Topical Review of machine learning in intracranial aneurysm surgery.- Review of applications of machine learning in neuroimaging.- Prediction Model.- An overview of machine learning applications in the Neurointensive Care Unit.- Prediction Model.- Review of natural language processing in the clinical neurosciences.- Review of big data applications in the clinical neurosciences.- Radiomic features associated with extent of resection in glioma surgery.
