Big Data in Psychiatry and Neurology
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Big Data in Psychiatry and Neurology

1st Edition - June 11, 2021
  • Editor: Ahmed Moustafa
  • Paperback ISBN: 9780128228845
  • eBook ISBN: 9780128230022

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Description

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

Readership

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

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

Product details

  • No. of pages: 384
  • Language: English
  • Copyright: © Academic Press 2021
  • Published: June 11, 2021
  • Imprint: Academic Press
  • Paperback ISBN: 9780128228845
  • eBook ISBN: 9780128230022

About the Editor

Ahmed Moustafa

Dr. Ahmed A. Moustafa is an associate professor in Cognitive and Behavioral Neuroscience at the Marcs Institute for Brain, Behavior, and Development, and in the School of Social Sciences and Psychology, Western Sydney University. He 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, he spent 11 years in America studying psychology and neuroscience. He researches computational and neuropsychological studies of addiction, schizophrenia, Parkinson’s disease, PTSD, and depression. He has published more than 150 papers in high-ranking journals including Science, PNAS, Journal of Neuroscience, Brain, Neuroscience and Biobehavioral Reviews, and Neuron.

Affiliations and Expertise

Associate Professor, Cognitive and Behavioral Neuroscience, Western Sydney University, Sydney, NSW, Australia; Department of Human Anatomy and Physiology, the Faculty of Health Sciences, University of Johannesburg, South Africa; Department of Psychiatry, Wroclaw Medical University, Wroclaw, Poland; School of Psychology and Marcs Institute for Brain and Behaviour, Western Sydney University, Sydney, NSW, Australia