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.- 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
1. Best practices for supervised machine learning when examining biomarkers in clinical populationsBenjamin G. Schultz, Zaher Joukhadar, Usha Nattala, Maria del Mar Quiroga, Francesca Bolk, and Adam P. Vogel2. Big data in personalized healthcareLidong Wang and Cheryl Alexander3. Longitudinal data analysis: The multiple indicators growth curve model approachThierno M.O. Diallo and Ahmed A. Moustafa4. Challenges and solutions for big data in personalized healthcareTim Hulsen5. Data linkages in epidemiologySinead Moylett6. Neutrosophic rule-based classification system and its medical applicationsSameh H. Basha, Areeg Abdalla, and Aboul Ella Hassanien7. From complex to neural networksNicola Amoroso and Loredana Bellantuono8. The use of Big Data in psychiatry—The role of administrative databasesManuel Goncalves-Pinho and Alberto Freitas9. Predicting the emergence of novel psychoactive substances with big dataRobert Todd Perdue and James Hawdon10. Hippocampus segmentation in MR images: Multiatlas methods and deep learning methodsHancan Zhu, Shuai Wang, Liangqiong Qu, and Dinggang Shen11. A scalable medication intake monitoring systemDiane Myung-Kyung Woodbridge and Kevin Bengtson Wong12. Evaluating cascade prediction via different embedding techniques for disease mitigationAbhinav Choudhury, Shubham Shakya, Shruti Kaushik, and Varun Dutt13. A two-stage classification framework for epileptic seizure prediction using EEG wavelet-based featuresSahar Elgohary, Mahmoud I. Khalil, and Seif Eldawlatly14. Visual neuroscience in the age of big data and artificial intelligenceKohitij Kar15. Application of big data and artificial intelligence approaches in diagnosis and treatment of neuropsychiatric diseasesQiurong Song, Tianhui Huang, Xinyue Wang, Jingxiao Niu, Wang Zhao, Haiqing Xu, and Long Lu16. Leveraging big data to augment evidence-informed precise public health responseG.V. Asokan and Mohammed Yousif Abbas Mohammed17. How big data analytics is changing the face of precision medicine in women's healthMaryam Panahiazar, Maryam Karimzadehgan, Roohallah Alizadehsani, Dexter Hadley, and Ramin E. Beygui
