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Machine Learning in Clinical Neuroscience : Foundations and Applications
other
Editor(s):
Victor E. Staartjes
,
Luca Regli
,
Carlo Serra
Publication date
(Print):
2022
Publisher:
Springer International Publishing
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Karger: Neurology and Neuroscience
Author and book information
Book
ISBN (Print):
978-3-030-85291-7
ISBN (Electronic):
978-3-030-85292-4
Publication date (Print):
2022
DOI:
10.1007/978-3-030-85292-4
SO-VID:
b5d4232b-4e07-4b2d-a689-fafef477daa4
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Book chapters
pp. 1
Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus
pp. 7
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part I—Introduction and General Principles
pp. 15
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II—Generalization and Overfitting
pp. 23
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part III—Model Evaluation and Other Points of Significance
pp. 33
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV—A Practical Approach to Binary Classification Problems
pp. 43
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V—A Practical Approach to Regression Problems
pp. 51
Foundations of Feature Selection in Clinical Prediction Modeling
pp. 59
Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience
pp. 65
A Discussion of Machine Learning Approaches for Clinical Prediction Modeling
pp. 75
Foundations of Bayesian Learning in Clinical Neuroscience
pp. 79
Introduction to Deep Learning in Clinical Neuroscience
pp. 91
Machine Learning-Based Clustering Analysis: Foundational Concepts, Methods, and Applications
pp. 101
Deployment of Clinical Prediction Models: A Practical Guide to Nomograms and Online Calculators
pp. 109
Updating Clinical Prediction Models: An Illustrative Case Study
pp. 115
Is My Clinical Prediction Model Clinically Useful? A Primer on Decision Curve Analysis
pp. 121
Introduction to Machine Learning in Neuroimaging
pp. 125
Machine Learning Algorithms in Neuroimaging: An Overview
pp. 139
Machine Learning-Based Radiomics in Neuro-Oncology
pp. 153
Foundations of Brain Image Segmentation: Pearls and Pitfalls in Segmenting Intracranial Blood on Computed Tomography Images
pp. 161
Applying Convolutional Neural Networks to Neuroimaging Classification Tasks: A Practical Guide in Python
pp. 171
Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging
pp. 183
Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning
pp. 195
Tackling the Complexity of Lesion-Symptoms Mapping: How to Bridge the Gap Between Data Scientists and Clinicians?
pp. 207
Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete
pp. 215
Foundations of Time Series Analysis
pp. 221
Overview of Algorithms for Natural Language Processing and Time Series Analyses
pp. 245
A Brief History of Machine Learning in Neurosurgery
pp. 251
Machine Learning and Ethics
pp. 257
The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI
pp. 263
Predictive Analytics in Clinical Practice: Advantages and Disadvantages
pp. 271
Big Data in the Clinical Neurosciences
pp. 277
Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review
pp. 291
Machine Learning in Pituitary Surgery
pp. 303
At the Pulse of Time: Machine Vision in Retinal Videos
pp. 313
Artificial Intelligence in Adult Spinal Deformity
pp. 319
Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction
pp. 333
Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery
pp. 341
Radiomic Features Associated with Extent of Resection in Glioma Surgery
pp. 349
Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer’s Disease, and Schizophrenia
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