Data Modelling and Analytics for the Internet of Medical Things (eBook, ePUB)

Redaktion: Pandey, Rajiv; Chiong, Raymond; Maurya, Pratibha

• Format: ePub

Produktbeschreibung

The emergence of the Internet of Medical Things (IoMT) is transforming the management of diseases, improving diseases diagnosis and treatment methods, and reducing healthcare costs and errors. This book covers all the essential aspects of IoMT in one place, providing readers with a comprehensive grasp of IoMT and related technologies.

Data Modelling and Analytics for the Internet of Medical Things integrates the architectural, conceptual, and technological aspects of IoMT, discussing in detail the IoMT, connected smart medical devices, and their applications to improve health outcomes. It explores various methodologies and solutions for medical data analytics in healthcare systems using machine learning and deep learning approaches, as well as exploring how technologies such as blockchain and cloud computing can further enhance data analytics in the e-health domain. Prevalent IoMT case studies and applications are also discussed.

This book is suitable for scientists, design engineers, system integrators, and researchers in the field of IoMT. It will also be of interest to postgraduate students in computer science focusing on healthcare applications and a supplementary reading for IoMT courses.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 328
• Erscheinungstermin: 22. Dezember 2023
• Englisch
• ISBN-13: 9781003825838
• Artikelnr.: 69570267

Autorenporträt

Rajiv Pandey, Senior Member IEEE, is a faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India. Pratibha Maurya is an assistant professor at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India. Raymond Chiong is currently an associate professor with the University of Newcastle, Australia.

Inhaltsangabe

Part I. IoMT Datasets and Storage. 1. Remote Health Monitoring in the Era
of the Internet of Medical Things. 2. Diabetic health care data analytics
and application. 3. Blockchain for Handling Medical Data. 4. Cloud
computing for complex IoMT data. 5. The potential of IoMT Devices in Early
Detection of Suicidal Ideation. Part II. Machine Learning for Medical
Things. 6. Artificial Intelligence and Internet of Medical Things in the
Diagnosis and Prediction of Disease. 7. Predicting Cardiovascular Diseases
Using Machine Learning: A Systematic Review of the Literature. 8.
Identification of Unipolar Depression Using Boosting Algorithms. 9.
Development of EEG based Identification of Learning Disability using
Machine Learning Algorithms. 10. Deep Learning Approaches for IoMT. 11
Machine Learning and Deep Learning Techniques to Classify Depressed
Patients from Healthy, Using Brain Signals from Electroencephalogram (EEG).
12. Dimensionality Reduction for IoMT Devices Using PCA. 13. Face Mask
Detection System. Part III. IoMT: Data Analytics and Use Cases. 14. An
IoT-based Real-time ECG Monitoring Platform for Multiple Patients. 15.
Study on Anomaly Detection in Clinical Laboratory Data Using Internet of
Medical Things. 16. Computational Intelligence Framework for Improving
Quality of Life in Cancer Patients. 17. Major Depressive Disorder Detection
using Data Science and Wearable Connected Devices.