Big Data in Psychiatry and Neurology

Ahmed Moustafa

• Publisher: Academic Press
• Year: 2021
• ISBN:
  9780128230022
• Topic: Biological Sciences
• Subtopic: Neuroscience
• Index: Biological Sciences

Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer's disease and Parkinson's disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients.

As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level.

• Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders
• Analyzes methods in using big data to treat psychiatric and neurological disorders
• Describes the role machine learning can play in the analysis of big data
• Demonstrates the various methods of gathering big data in medicine
• Reviews how to apply big data to genetics

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Contributors
• Editor's biography
• Preface
• Acknowledgment
• Chapter 1: Best practices for supervised machine learning when examining biomarkers in clinical populations
• Chapter 2: Big data in personalized healthcare
• Chapter 3: Longitudinal data analysis: The multiple indicators growth curve model approach
• Chapter 4: Challenges and solutions for big data in personalized healthcare
• Chapter 5: Data linkages in epidemiology
• Chapter 6: Neutrosophic rule-based classification system and its medical applications
• Chapter 7: From complex to neural networks
• Chapter 8: The use of Big Data in Psychiatry—The role of administrative databases
• Chapter 9: Predicting the emergence of novel psychoactive substances with big data
• Chapter 10: Hippocampus segmentation in MR images: Multiatlas methods and deep learning methods
• Chapter 11: A scalable medication intake monitoring system
• Chapter 12: Evaluating cascade prediction via different embedding techniques for disease mitigation
• Chapter 13: A two-stage classification framework for epileptic seizure prediction using EEG wavelet-based features
• Chapter 14: Visual neuroscience in the age of big data and artificial intelligence
• Chapter 15: Application of big data and artificial intelligence approaches in diagnosis and treatment of neuropsychiatric diseases
• Chapter 16: Harnessing big data to strengthen evidence-informed precise public health response
• Chapter 17: How big data analytics is changing the face of precision medicine in women’s health
• Index