Internet of Things and Machine Learning for Smart Healthcare

The digitization of patient data by health services has marked a significant milestone in enhancing patient care. The next frontier in precision healthcare is seamlessly integrating this digitized data with real-time data streams from Internet of Things (IoT) devices. This integration is poised to foster the development of Personal Health Records, culminating in a decentralized, interoperable, and comprehensive health history that can be shared across institutions. These integrated datasets become a valuable resource for machine learning algorithms, providing insights for monitoring, diagnosis, personalized treatment, drug discovery, and optimizing clinical trials. However, the diverse data sources present a complex technical challenge in the organizational, legal, and regulatory landscapes. In this chapter, we explore the technological intricacies of the Internet of Health Things, examining the current capabilities, limitations, and prospective applications of these technologies in smart healthcare. We provide a technical discussion of data integration methods, including interoperability standards and distributed ledger technologies that can ensure data integrity and traceability. Furthermore, we explore the use of machine learning models which can handle vast and heterogeneous datasets. Finally, we address the main benefits for the patient and health institutions in using collaborative data-sharing architectures based on IoT and machine learning.