Psychopathology Early Prediction and Classification

A Deep Generative AI Approach
1. Auflage 2026
ISBN: 978-1-394-33673-9
Verlag: John Wiley & Sons Inc

Buch, Englisch, 784 Seiten

Unlock the full potential of artificial intelligence in mental health with this definitive guide to combining multiple machine learning models for reproducible biomarker discovery and predictive treatment modeling.

Advancing Psychopathology Diagnosis and Treatment: The Power of Ensemble Learning delves into the transformative potential of ensemble learning techniques in the field of psychopathology. This comprehensive book provides an in-depth exploration of how combining multiple machine learning models can enhance the accuracy of diagnoses, predict treatment outcomes, and refine therapeutic interventions for a variety of mental health disorders, including depression, anxiety, schizophrenia, and bipolar disorder. Key sections of the book examine the capabilities of ensemble learning in developing personalized treatment strategies that cater to individual patient needs and predicting treatment responses. It also explores the use of these advanced algorithms for biomarker discovery, enhancing the reproducibility of identifying biological indicators linked to mental health conditions. The book discusses the practical aspects of implementing these technologies in clinical settings, including integration with existing healthcare systems and clinician training. Through a blend of theoretical insights and practical examples, this book is an essential resource for clinicians, researchers, and policymakers involved in mental health care, offering innovative solutions and fostering a deeper understanding of how artificial intelligence can be harnessed to improve patient outcomes in psychopathology.

Autoren/Hrsg.

Kumar, Abhishek

Singh Rathore, Pramod

Ahuja, Sachin

Rahi, Pankaj

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Psychiatrie, Sozialpsychiatrie, Suchttherapie

Inhaltsverzeichnis

Preface xxvii

1 Assessment of Well-Being Among Undergraduate Students Using PERMA+ Model 1
Mihir Vakhariya and Supriya S. Patil

2 Deep Generative AI for Psychopathology Classification and Children's Handwriting Recognition 25
Bintul Huda

3 AI-Driven Drug Discovery and Personalized Medicine in Cancer 47
Suresh Bhosale and Vibha Vyas

4 Integration of Multiomics Data for Precision Cancer Research 67
Suresh Bhosale and Vibha Vyas

5 Leveraging RNNs for Cancer Time-Series Data Analysis 89
Suresh Bhosale, Vibha Vyas, Saif M.B. Al Sabti and Raid Gaib

6 Designing Nanobots for Targeted Drug Delivery and the Application of Nanotechnology in Modern Medical Advancements 109
Jayant Pawar, Kalpana Malpe, Saif M.B. Al Sabti and Raid Gaib

8 Evaluating Precision of Artificial Intelligence–Driven Robotic Assistants in Complex Surgeries within Robotic Surgery Healthcare Advancements 165
Sweta Colvin and Vibha Vyas

9 Enhancing Cancer Diagnosis through Artificial Intelligence–Assisted Histopathology in Digital Pathology Applications for Healthcare Management 191
Anand Gudur, Pawar Atul Namdev, Saif M.B. Al Sabti and Raid Gaib

10 Developing Smart Hydrogels for Wound Care and Drug Delivery Applications with Hydrogel Technology Advancements in Healthcare Systems 215
Asma A. Hussain, Garagate Amruta K., Saif M.B. Al Sabti and Raid Gaib

11 AI-Enhanced Image Analysis for Chronic Disease Diagnosis: AI-Driven Advancements in Medical Imaging (MRI, CT, X-Rays) for Diagnosing Chronic Diseases Like Cancer and Liver Cirrhosis 243
Prakash Patil and Rasika Ranjit Chafle

12 AI in Chronic Kidney Disease: Monitoring, Prediction, and Prevention How AI Helps Track Kidney Function, Predict Progression, and Optimize Dialysis Treatments 267
Anil Huddedar and K. Gavhale

13 Advanced Survival Analysis for Cancer Prognostics Using AI 289
Sujata Sanjay Kumbhar

14 AI-Driven Innovations in Cancer Screening and Detection 309
N. J. Patil

15 Exploring Transfer Learning for Improved Cancer Diagnostics 329
Avinash Mane

16 Integrating AI and Multiomics for Breakthroughs in Cancer Studies 349
Atul Bhanudas Hulwan

17 Model Evaluation and Validation Strategies in Cancer Research 369
Anand Gudur

18 Multiomics Data Integration: Transforming Cancer Research 387
Sujata Raghunath Kanetkar

19 RNN Applications in Longitudinal Cancer Data Analysis 409
Rashmi Gudur

20 The Role of Transfer Learning in Accelerating Cancer Research 431
Kailas Datkhile

21 Pioneering Innovations in Healthcare and Energy: A Deep Generative AI Approach to Early Psychopathology Prediction and Adaptive Series Algorithm Management for Green Hydrogen Market Analysis 453
Ibrahim Kadriinamdar, Manisha Paliwal, Alamgir Sani, Kumari Lipi and Madhuranjan Vatsa

22 Building the Foundation: Machine Learning's Impact on Mental Health Diagnostics 475
K. Palani, Jothikumar R., M. Nagarajan, E. Sivarajan, S. Sathya and Gouri M.S.

23 Ensemble Learning Approaches to Enhance Personalized Medicine in Treatment Planning 499
K. Palani, Jothikumar R., Sherin Eliyas, E. Sivarajan, M. Nagarajan and Gouri M. S.

24 Ensemble Learning in Psychopathology: A New Era of Predictive Analysis 519
Susi S., Mohammed Waheeduddin Hussain, Sathish Kumar M., M. Ravichandran, E. Sivarajan, M. Nagarajan and Jayendra Kumar

25 Applications of AI in Early Diagnosis of Neurodevelopmental Disabilities: A Deep Learning Approach 541
Durai Vasanth R., S. Varadharajan, Suganya. K., Harishchander Anandaram, Shreenidhi K.S. and B. Prameela Rani

26 Advancing Neuroimaging with Deep Learning: Principles, Techniques and Applications 563
S. Murugaanandam, N. Elamathi, Harishchander Anandaram, Shreenidhi K. S., Priya V. and B. Prameela Rani

27 Managing and Analyzing Large Neurological Datasets: Challenges and AI-Driven Solutions 581
V. Ceronmani Sharmila, M. Jenath, V. Sumitra, S. Sunithamani, J. Abanah Shirley and S. N. Lakshmi Malluvalasa

28 AI-Powered Mental Healthcare: Investigating the Role of Generative Deep Learning Models in Personalized Treatment and Cognitive Behavioral Therapy 601
R. Prasanna, M. Jenath, D. Tharani, Lakshmi Thara R., Arumbu V. N. and Sankar Ganesh Karuppasamy

29 Mental Illness with Deep Learning 623
Kumud Sachdeva and Ayush Mahanta

30 Psychopathology and AI in Mental Health 671
Priya Batta and Arjun Singh

31 Generative AI Models for Mental Health Diagnosis and Therapy: A Comparative Analysis of GANs, VAEs, and Transformer-Based Approaches 687
M. Jenath, V. Sumitra, V. Ceronmani Sharmila, J. Abanah Shirley, S. Sunithamani and T. Sajana

References 705
Index 707

Über Autor(innen)

Abhishek Kumar, PhD is currently working an Associate Professor at Manipal University. He has more than 100 publications in reputed, peer-reviewed national and international journals, books, and conferences. His research interests include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning.

Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University with more than 11 years of academic experience. He has published more than 55 papers in reputable, peer-reviewed national and international journals, books, and conferences and co-authored and edited numerous books with well-known publishers. His research interests include NS2, computer networks, mining, and DBMS.

Sachin Ahuja, PhD is a Professor and Executive Director of UIE at Chandigarh University. He has led multiple funded research projects in artificial intelligence, machine learning, and data mining and has contributed to numerous academic books. He has also served as a guest editor for special issues in reputed international journals.

Pankaj Rahi, PhD is an Associate Professor in Health Information Technology Management at the Indian Institute of Health Management Research with more than 17 years of experience. He has published more than 15 research papers and book chapters, one book, and ten patents. His research experience is in smart eHealth systems, brain technology interface, cloud and big data analytics, machine learning, and artificial intelligence.