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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xv 8. Editor's biography - p. xvii 9. Preface - p. xvii 10. Acknowledgment - p. xvii 11. Chapter 1. Best practices for supervised machine learning when examining biomarkers in clinical populations - p. 3 12. Chapter 2. Big data in personalized healthcare - p. 35 13. Chapter 3. Longitudinal data analysis: The multiple indicators growth curve model approach - p. 51 14. Chapter 4. Challenges and solutions for big data in personalized healthcare - p. 69 15. Chapter 5. Data linkages in epidemiology - p. 95 16. Chapter 6. Neutrosophic rule-based classification system and its medical applications - p. 119 17. Chapter 7. From complex to neural networks - p. 137 18. Chapter 8. The use of Big Data in Psychiatry—The role of administrative databases - p. 155 19. Chapter 9. Predicting the emergence of novel psychoactive substances with big data - p. 167 20. Chapter 10. Hippocampus segmentation in MR images: Multiatlas methods and deep learning methods - p. 181 21. Chapter 11. A scalable medication intake monitoring system - p. 225 22. Chapter 12. Evaluating cascade prediction via different embedding techniques for disease mitigation - p. 241 23. Chapter 13. A two-stage classification framework for epileptic seizure prediction using EEG wavelet-based features - p. 263 24. Chapter 14. Visual neuroscience in the age of big data and artificial intelligence - p. 287 25. Chapter 15. Application of big data and artificial intelligence approaches in diagnosis and treatment of neuropsychiatric diseases - p. 305 26. Chapter 16. Harnessing big data to strengthen evidence-informed precise public health response - p. 325 27. Chapter 17. How big data analytics is changing the face of precision medicine in women’s health - p. 339 28. Index - p. 351
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xv 8. Editor's biography - p. xvii 9. Preface - p. xvii 10. Acknowledgment - p. xvii 11. Chapter 1. Best practices for supervised machine learning when examining biomarkers in clinical populations - p. 3 12. Chapter 2. Big data in personalized healthcare - p. 35 13. Chapter 3. Longitudinal data analysis: The multiple indicators growth curve model approach - p. 51 14. Chapter 4. Challenges and solutions for big data in personalized healthcare - p. 69 15. Chapter 5. Data linkages in epidemiology - p. 95 16. Chapter 6. Neutrosophic rule-based classification system and its medical applications - p. 119 17. Chapter 7. From complex to neural networks - p. 137 18. Chapter 8. The use of Big Data in Psychiatry—The role of administrative databases - p. 155 19. Chapter 9. Predicting the emergence of novel psychoactive substances with big data - p. 167 20. Chapter 10. Hippocampus segmentation in MR images: Multiatlas methods and deep learning methods - p. 181 21. Chapter 11. A scalable medication intake monitoring system - p. 225 22. Chapter 12. Evaluating cascade prediction via different embedding techniques for disease mitigation - p. 241 23. Chapter 13. A two-stage classification framework for epileptic seizure prediction using EEG wavelet-based features - p. 263 24. Chapter 14. Visual neuroscience in the age of big data and artificial intelligence - p. 287 25. Chapter 15. Application of big data and artificial intelligence approaches in diagnosis and treatment of neuropsychiatric diseases - p. 305 26. Chapter 16. Harnessing big data to strengthen evidence-informed precise public health response - p. 325 27. Chapter 17. How big data analytics is changing the face of precision medicine in women’s health - p. 339 28. Index - p. 351
