Knowledge Modelling and Big Data Analytics in Healthcare
Advances and Applications
- Available for pre-order. Item will ship after November 29, 2021
This book focuses on automated analytical techniques for healthcare applications used to extract knowledge from a large amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals.
Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications connects four contemporary areas of research rarely brought together in one book: artificial intelligence, big data analytics, knowledge modelling, and healthcare. It presents state-of-the-art research from the healthcare sector, including research on medical imaging, healthcare analysis, and the applications of artificial intelligence in drug discovery.
This book is intended for data scientists, academicians, and industry professionals in the healthcare sector.
Table of Contents
Section I: Big Data Analytics in Medical Imaging. 1. Medical Imaging: Computer-Based Representation, Processing and Analysis of Images. 2. Disease Identification and Prediction Using Machine Learning. 3. MRI-Based Image Analysis for Brain Tumor Classification. 4. Computer-Aided Diagnosis of Lung Diseases Using Deep Learning Models. 5. Diagnosis of Neurological/ Psychological Diseases (Mental Disorder) Using Neuroimaging Data. Section II: Knowledge Mining in Healthcare. 6. Knowledge Modelling of Healthcare Data. 7. Decision Support, Knowledge Representation and Management in Healthcare. 8. Clinical Decision Support Systems. 9. Management of Big Medical Data: Preprocessing Pipeline and Usage of Docker in Medical Data. 10. Advancements and Future Possibilities in Knowledge Modelling and Information Integration in Healthcare. Section III: AI for Genetics and Genomics. 11. Analysis of Microarray Data Using Artificial Intelligence. 12. Big Data Analytics in Genomics to Unlock New Therapies. 13. AI-Inspired Technique for Optimized RNA and DNA Sequencing. 14. Genetics and Genomics of Parkinson’s Disease. 15. Deep Learning Application for Disease Identification Using Gene Expressions. Section IV: Robot Assisted Medical Intervention. 16. Surgical Robotics: From Open Surgery to Minimally Invasive and Robot-Assisted Surgery. 17. Application of Machine Learning in Surgical Robots. 18. Case Study: Minimally Invasive Robot Assisted Surgery. 19. Challenges in Surgical Robot Development. Section V: AI for Drug Discovery. 20. Machine Learning for the Development of Drugs and Vaccine. 21. Deep Learning for Prediction of Drug-Target Interaction. 22. Challenges and Research Opportunities in AI-Driven Drug Discovery. Section VI: IoT and Big Data for Smart Healthcare. 23. Smart Healthcare: Challenges and Potential Solutions Using Internet of Things (IoT) and Big Data Analytics. 24. Internet of Things in Healthcare: Use Cases. 25. The IoT-Based Personal Health Monitoring System. 26. Wearable Devices and Future of Smart Healthcare.
Mayuri Mehta is a passionate learner, teacher and researcher. She is working as a Professor in the Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Surat, India. She received her Ph.D. in Computer Engineering from Sardar Vallabhbhai National Institute of Technology (SVNIT), India. Her areas of teaching and research include Data Science, Machine Learning & Deep Learning, Health Informatics, Computer Algorithms, and Python Programming. She has worked on several academic assignments in collaboration with professors of universities across the globe. Her 20 years of professional experience includes several academic and research achievements along with administrative and organizational capabilities. She has also co-edited a book titled "Tracking and Preventing Diseases using Artificial Intelligence". With the noble intention of applying her technical knowledge for societal impact, she is working on several research projects in the Healthcare domain in association with doctors doing private practice and doctors of Medical Colleges, which reflect her research outlook. She is an active member of professional bodies such as IEEE, Computer Society of India (CSI) and Indian Society for Technical Education (ISTE).
Kalpdrum Passi received his Ph.D. in Parallel Numerical Algorithms from Indian Institute of Technology, Delhi, India in 1993. He is an Associate Professor, Department of Mathematics & Computer Science, at Laurentian University, Ontario, Canada. He has published many papers on Parallel Numerical Algorithms in international journals and conferences. He has collaborative work with faculty in Canada and US and the work was tested on the CRAY XMP’s and CRAY YMP’s. He transitioned his research to web technology, and more recently has been involved in machine learning and data mining applications in bioinformatics, social media and other data science areas. His research in bioinformatics has been on improving the accuracy of predicting diseases such as different types of cancer using microarray data. He has published several papers related to prediction of cancer using microarray data and epigenomic data. He obtained funding from NSERC and Laurentian University for his research. He is a member of the ACM and IEEE Computer Society.
Indranath Chatterjee is working as a Professor in the Department of Computer Engineering at Tongmyong University, Busan, South Korea. He received his Ph. D. in Computational Neuroscience from the Department of Computer Science, University of Delhi, Delhi, India. His research areas include Computational Neuroscience, Medical Imaging, Data Science, Machine Learning, and Computer Vision. He is the author of four textbooks on computer science and published numerous scientific articles in renowned international journals and conferences. He is currently serving as a Chief Section Editor of the Neuroscience Research Notes journal and serving as a member of the Advisory board and Editorial board of various international journals and Open-Science organizations worldwide. He is presently working on several projects of government & non-government organizations as PI/co-PI, related to medical imaging and machine learning for a broader societal impact, in collaboration with more than 15 universities globally. He is an active professional member of the Association of Computing Machinery (ACM, USA), Organization of Human Brain Mapping (OHBM, USA), Federations of European Neuroscience Society (FENS, Belgium), International Association of Neuroscience (IAN, India), and International Neuroinformatics Coordinating Facility (INCF, Sweden).
Rajan Patel is currently working as a Professor at Gandhinagar Institute of Technology, Gandhinagar, Gujarat, India. He received a Ph.D. degree in Computer Engineering from R. K. University, Rajkot, India and M.Tech. in Computer Engineering from S. V. National Institute of Technology (NIT), Surat, India. He has more than 16 years of teaching experience in the field of Computer Science and Engineering and research experience mainly in the domain of Networking, Security and Intelligent Applications. He has more than 51 collaborative publications in journals and conferences, and presented 17 articles in National/International conferences including IEEE, Science Direct, Springer, Elsevier. As a coauthor, he has published an edited International book entitled "Data Science and Intelligent Applications" in Springer's LNDECT series. He also worked for ISEAP sponsored MHRD funded project during his Post graduation period at NIT, Surat, India. He is a member of professional bodies CSI, ISTE and UACEE. He has also received numerous awards, honors and certificates of excellence. His main area of interest includes AI, Data Science, and Intelligent Communication and its Security.