Big Data in Psychiatry and Neurology

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.

Autorentext

Dr. Ahmed Moustafa is the Head of School of Psychology and Professor of Psychology and Computational Modeling at Bond University, Australia. He obtained his BSc in Mathematics and Computer Science at Cairo University, Egypt, and his PhD in Cognitive Science at the University of Lafayette, USA. Dr. Moustafa specializes in computational and neuropsychological studies of addiction, schizophrenia, Parkinson's disease, PTSD, depression, and Alzheimer's disease. He is the Editor-in-Chief of Discover Psychology (Springer) and has edited ten books, including Elsevier's Cognitive, Clinical, and Neural Aspects of Drug Addiction; The Psychology and Neuroscience of Impulsivity; Cognitive and Behavioral Dysfunction in Schizophrenia; Mental Health Effects of COVID-19; Alzheimer's Disease; Cybersecurity and Cognitive Science; Big Data in Psychiatry and Neurology; The Nature of Depression; and Social Cognition in Psychosis.

Klappentext

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.

Inhalt

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