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Medicine & Public Health
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Chapter and Conference Paper
The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI
The applications of artificial intelligence (AI) and machine learning (ML) in modern medicine are growing exponentially, and new developments are fast-paced. However, the lack of trust and appropriate legislat...
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Chapter and Conference Paper
Machine Learning in Pituitary Surgery
Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome,...
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Chapter and Conference Paper
Artificial Intelligence in Adult Spinal Deformity
Artificial Intelligence is gaining traction in medicine for its ease of use and advancements in technology. This study evaluates the current literature on the use of artificial intelligence in adult spinal def...
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Chapter and Conference Paper
Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery
Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insi...
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Chapter and Conference Paper
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part I—Introduction and General Principles
We provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modeling, which is the focus of this series. In part...
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Chapter and Conference Paper
Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer’s Disease, and Schizophrenia
Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in...
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Chapter and Conference Paper
Foundations of Bayesian Learning in Clinical Neuroscience
There is an increasing interest in using prediction models to forecast clinical outcomes within the fields of neurosurgery and clinical neuroscience. The present chapter outlines the foundations of Bayesian le...
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Chapter and Conference Paper
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part III—Model Evaluation and Other Points of Significance
Various available metrics to describe model performance in terms of discrimination (area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 Scor...
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Chapter and Conference Paper
Machine Learning-Based Clustering Analysis: Foundational Concepts, Methods, and Applications
Unsupervised learning, the task of clustering observations in such a way that observations within cluster are more similar than those assigned to other clusters is one the central tasks of data science. Its ex...
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Chapter and Conference Paper
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V—A Practical Approach to Regression Problems
This chapter goes through the steps required to train and validate a simple, machine learning-based clinical prediction model for any continuous outcome. We supply fully structured code for the readers to down...
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Chapter and Conference Paper
Updating Clinical Prediction Models: An Illustrative Case Study
The performance of clinical prediction models tends to deteriorate over time. Researchers often develop a new prediction if an existing model performs poorly at external validation. Model updating is an effici...
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Chapter and Conference Paper
Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience
Advancements in population neuroscience are spurred by the availability of large scale, open datasets, such as the Human Connectome Project or recently introduced UK Biobank. With the increasing data availabil...
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Chapter and Conference Paper
Introduction to Machine Learning in Neuroimaging
Advancements in neuroimaging and the availability of large-scale datasets enable the use of more sophisticated machine learning algorithms. In this chapter, we non-exhaustively discuss relevant analytical step...
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Chapter and Conference Paper
Applying Convolutional Neural Networks to Neuroimaging Classification Tasks: A Practical Guide in Python
In this chapter, we describe the process of obtaining medical imaging data and its storage protocol. The authors also explain in a step-by-step approach how to extract and prepare the medical imaging data for ...
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Chapter and Conference Paper
Machine Learning-Based Radiomics in Neuro-Oncology
In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceeding human comprehension. With improved computational methods...
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Chapter and Conference Paper
Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning
The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valu...
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Chapter and Conference Paper
Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete
Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are g...
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Chapter and Conference Paper
Overview of Algorithms for Natural Language Processing and Time Series Analyses
A host of machine learning algorithms have been used to perform several different tasks in NLP and TSA. Prior to implementing these algorithms, some degree of data preprocessing is required. Deep learning appr...
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Chapter and Conference Paper
Predictive Analytics in Clinical Practice: Advantages and Disadvantages
Predictive analytics are increasingly reported by clinicians. These tools aim to improve patient outcomes in terms of quality, safety, and efficiency. However, deploying predictive analytics in clinical practi...
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Chapter and Conference Paper
When Is Diagnostic Subtraction Angiography Indicated Before Clip** of Unruptured and Ruptured Intracranial Aneurysms? An International Survey of Current Practice
Introduction: The goal of this survey is to investigate the indications for preoperative digital subtraction angiography (DSA) before clip** of ruptured and unruptured intracranial aneurysms in an international...
