Artificial Intelligence in e-Health Framework, Volume 1 AI, Classification, Wearable Devices, Computer-Aided Diagnosis, E-Health Records

Langue : Anglais

Coordonnateurs : Paul Sudip, Suri Jasjit

Couverture de l’ouvrage Artificial Intelligence in e-Health Framework, Volume 1

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Artificial Intelligence in e-health Framework, Volume 1: Classification, Wearable Devices, Computer-Aided Diagnosis, E-Health Records presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of machine and deep learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition and diagnostics, as well as e-health records management. It is a valuable resource for health professionals, scientists and researchers, students, and all who wish to broaden their knowledge in this advancing technology.

Section 1: Introduction to Artificial Intelligence 1. Data Processing 2. Regression Algorithm 3. Classification Algorithm 4. Clustering Algorithm 5. Deep Learning Section 2: Application of Artificial Intelligence in Disease Diagnosis 6. Disease Detection 7. Disease Classification 8. Improving the Decision-making Process 9. AI-based Treatment Solutions 10. Application of AI in Big Data Management Section 3: AI in Health Monitoring and Wearables Devices 11. Remote Heart Monitoring with AI 12. Predicting Women’s Fertility with AI 13. AI Wearable for the Visually Impaired 14. AI Wearable for Deaf and Dumb 15. AI for Chronic Illness Patients Section 4: Application of AI Medical Image Recognition 16. Identifying Cardiovascular Abnormalities 17. Detecting Fractures and other Musculoskeletal Injuries 18. Aiding in the Diagnosis Neurological Diseases 19. Flagging Thoracic Complications and Conditions 20. Screening for Common Cancers Section 5: AI in E-Health Records Management 21. Integration of Personal Records in AI Framework 22. Automated Extraction of Patient Information using AI 23. AI to Identify Patterns and Perform Outcome Predictions 24. AI-based Clinical Decision Support (CDS) 25. AI in Healthcare Natural Language

Dr. Sudip Paul, Post-Doctoral Fellow and PhD, is currently an Assistant Professor & Teacher In-Charge in the Department of Biomedical Engineering, School of Technology, North-Eastern Hill University (NEHU), Shillong, India. He has published over 40 journal papers, over 35 conference papers, and has contributed his knowledge as editorial board member and reviewer for multiple international journals. He has been granted one patent of eight filled and completed more than ten book projects. Dr. Sudip has presented his research accomplishments in countries around the world. He is a member of multiple societies and professional bodies, including APSN, ISN, IBRO, SNCI, SfN, IEEE, IAS. Dr. Sudip has received many awards, including the World Federation of Neurology (WFN) traveling fellowship, Young Investigator Award, IBRO Travel Awardee, and ISN Travel Awardee.

Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General’s Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.

Provides an in-depth introduction to Artificial Intelligence in e-health framework
Reviews theoretical and application information to develop understanding of AI advances in diagnostics, health monitoring, and records management
Discusses advanced AI techniques in both machine learning and deep learning for solving healthcare industry issues