Big Data in Psychiatry and Neurology,
Edition 1
Edited by Ahmed Moustafa, Ph.D

Publication Date: 16 Jun 2021

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.

Key Features

  • 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
About the author
Edited by Ahmed Moustafa, Ph.D, School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, Queensland, Australia.
Table of Contents

1. Best practices for supervised machine learning when examining biomarkers in clinical populations
Benjamin G. Schultz, Zaher Joukhadar, Usha Nattala, Maria del Mar Quiroga, Francesca Bolk, and Adam P. Vogel

2. Big data in personalized healthcare
Lidong Wang and Cheryl Alexander

3. Longitudinal data analysis: The multiple indicators growth curve model approach
Thierno M.O. Diallo and Ahmed A. Moustafa

4. Challenges and solutions for big data in personalized healthcare
Tim Hulsen

5. Data linkages in epidemiology
Sinead Moylett

6. Neutrosophic rule-based classification system and its medical applications
Sameh H. Basha, Areeg Abdalla, and Aboul Ella Hassanien

7. From complex to neural networks
Nicola Amoroso and Loredana Bellantuono

8. The use of Big Data in psychiatry—The role of administrative databases
Manuel Goncalves-Pinho and Alberto Freitas

9. Predicting the emergence of novel psychoactive substances with big data
Robert Todd Perdue and James Hawdon

10. Hippocampus segmentation in MR images: Multiatlas methods and deep learning methods
Hancan Zhu, Shuai Wang, Liangqiong Qu, and Dinggang Shen

11. A scalable medication intake monitoring system
Diane Myung-Kyung Woodbridge and Kevin Bengtson Wong

12. Evaluating cascade prediction via different embedding techniques for disease mitigation
Abhinav Choudhury, Shubham Shakya, Shruti Kaushik, and Varun Dutt

13. A two-stage classification framework for epileptic seizure prediction using EEG wavelet-based features
Sahar Elgohary, Mahmoud I. Khalil, and Seif Eldawlatly

14. Visual neuroscience in the age of big data and artificial intelligence
Kohitij Kar

15. Application of big data and artificial intelligence approaches in diagnosis and treatment of neuropsychiatric diseases
Qiurong Song, Tianhui Huang, Xinyue Wang, Jingxiao Niu, Wang Zhao, Haiqing Xu, and Long Lu

16. Leveraging big data to augment evidence-informed precise public health response
G.V. Asokan and Mohammed Yousif Abbas Mohammed

17. How big data analytics is changing the face of precision medicine in women‘s health
Maryam Panahiazar, Maryam Karimzadehgan, Roohallah Alizadehsani, Dexter Hadley, and Ramin E. Beygui

Book details
ISBN: 9780128228845
Page Count: 384
Retail Price : £118.00

9780124202481; 9780128001059; 9780128131763; 9780124171145; 9780128037843; 9780128122020; 9780124158047


Researchers and students in Psychiatry and Neurology designing protocols; Clinicians involved in clinical trials