Big Data in Psychiatry and Neurology

1st Edition - June 11, 2021
Imprint: Academic Press
Editor: Ahmed Moustafa
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 8 8 4 - 5
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 0 0 2 - 2

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 meth… Read more

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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.

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
Abstract
1: Introduction
2: Data formatting
3: Statistical assumptions
4: Sample size estimation
5: Choosing parsimonious models
6: Reduction of data dimensionality
7: Performance metrics
8: Resampling methods
9: Data leakage
10: Supervised machine learning classifiers
11: Deep learning and artificial intelligence
12: Limitations and future directions
13: Conclusions
Chapter 2: Big data in personalized healthcare
Abstract
Acknowledgment
1: Introduction
2: Characteristics, methods, and software platforms of big data
3: Big data in the healthcare area
4: Big data and big data analytics in personalized healthcare
5: Conclusion
Chapter 3: Longitudinal data analysis: The multiple indicators growth curve model approach
Abstract
1: Introduction
2: Multivariate dimension reduction techniques: Principal component analysis and factor analysis
3: Longitudinal measurement invariance
4: Multiple indicators growth curve model
5: Steps in fitting an MILCM
Chapter 4: Challenges and solutions for big data in personalized healthcare
Abstract
1: Introduction
2: Standardization
3: Data sharing and integration
4: Privacy and ethics
5: Teaching data science
6: Discussion
Competing interest statement
Chapter 5: Data linkages in epidemiology
Abstract
1: Introduction
2: Linking local and national routinely-collected data
3: Linking routinely- and non-routinely-collected data
4: Linking structured and unstructured routinely-collected data
5: Conclusion
Chapter 6: Neutrosophic rule-based classification system and its medical applications
Abstract
1: Introduction
2: Theoretical background
3: NRCS medical applications
4: Conclusions and future work
Chapter 7: From complex to neural networks
Abstract
1: Big data and MRI analyses
2: Modeling purposes: Complex networks
3: Learning from data
4: A multiplex model to diagnose neurodegenerative diseases and anomalous aging
Chapter 8: The use of Big Data in Psychiatry—The role of administrative databases
Abstract
1: Introduction
2: Big Data, administrative databases, and mental health
3: Pros and cons of administrative databases research in mental health
4: Conclusions
Chapter 9: Predicting the emergence of novel psychoactive substances with big data
Abstract
1: Introduction
2: Internet search queries as data
3: Methods
4: Results
5: Discussion and conclusion
Chapter 10: Hippocampus segmentation in MR images: Multiatlas methods and deep learning methods
Abstract
Acknowledgments
1: Introduction
2: Patch-based multiatlas labeling for Hippocampus segmentation
3: Deep learning-based methods for Hippocampus segmentation
4: Conclusion
Chapter 11: A scalable medication intake monitoring system
Abstract
Acknowledgments
1: Introduction
2: Related work
3: System architecture
4: Algorithms
5: Experiment results
6: Conclusion
Chapter 12: Evaluating cascade prediction via different embedding techniques for disease mitigation
Abstract
Acknowledgment
1: Introduction
2: Background
3: Method
4: Results
5: Discussion and conclusions
Chapter 13: A two-stage classification framework for epileptic seizure prediction using EEG wavelet-based features
Abstract
1: Introduction
2: Materials and methods
3: Results
4: Discussion
5: Conclusions
Chapter 14: Visual neuroscience in the age of big data and artificial intelligence
Abstract
1: Confining the problem space
2: Chapter roadmap
3: Understanding vision—What do we seek to reveal?
4: How to evaluate the current models of vision?
5: The vision community is coming together to combine data and models
6: Conclusion
Chapter 15: Application of big data and artificial intelligence approaches in diagnosis and treatment of neuropsychiatric diseases
Abstract
1: Introduction
2: Main data sources
3: Main algorithms
4: Applications
5: Challenges and promising solutions
6: Conclusions
Chapter 16: Harnessing big data to strengthen evidence-informed precise public health response
Abstract
1: Public health
2: Global burden of disease
3: Health systems and public health system
4: Big data in precision public health
5: Case studies
Chapter 17: How big data analytics is changing the face of precision medicine in women’s health
Abstract
1: Introduction
2: The role of big data and deep learning in personalized medicine to empower women’s health
3: Use case studies
4: Conclusion
Index

Ahmed Moustafa

Dr. Ahmed Moustafa is a Professor of Psychology and Computational Modeling at School of Psychology, Bond University, Gold Coast, Queensland, Australia. Prior to moving to Bond University, Ahmed was an associate professor in Psychology and Neuroscience at Marcs Institute for Brain, Behavior, and Development & School of Psychology, Western Sydney University. Ahmed is trained in computer science, psychology, neuroscience, and cognitive science. His early training took place at Cairo University in mathematics and computer science. Before joining Western Sydney University as a lab director, Ahmed spent 11 years in America working on several psychology and neuroscience projects. Ahmed conducts research on computational and neuropsychological studies of addiction, schizophrenia, Parkinson’s disease, PTSD, depression, Alzheimer’s disease. He has published over 240 papers in high-ranking journals including Science, PNAS, Journal of Neuroscience, Brain, Neuroscience and Biobehavioral Reviews, Nature (Parkinson’s disease), Neuron, among others. Ahmed has obtained grant funding from Australia, USA, Qatar, UAE, Turkey, and other countries. Ahmed has recently published ten books: (1) Computational models of brain and behavior; (2) Social Cognition in Psychosis, (3) computational Neuroscience Models of the Basal Ganglia, (4) Cognitive, Clinical, and Neural Aspects of Drug Addiction; (5) The Nature of Depression: An updated review; (6) Big data in psychiatry and neurology; (7) Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy. Elsevier; (8) Cognitive and Behavioral Dysfunction in Schizophrenia; (9) Female Pioneers from Ancient Egypt and the Middle East; and (10) Mental health effects of COVID-19. In the last 10 years, Ahmed has published collaboratively with 71 colleagues, has more than 510 co-authors, from 35 institutions in 14 countries. Ahmed is now Editor-in-Chief of Discover Psychology, a new journal by Springer Nature.

Affiliations and expertise

School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, Queensland, Australia

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