Health monitoring and diagnosis of the target structure of interest are achieved through the interpretation of the collected data on the Internet of Things. The rapid advances in wearable medical devices, technologies, and data acquisition tools have led to the new era of big data, where substantial and diverse data are collected by different sensors. This large volume of health data, often called big medical data, cannot readily be processed by traditional data processing algorithms and applications. Internet of Medical Things (IoMT) can enhance the decision-making process and early disease diagnosis for future healthcare systems. Hence, there is a need for scalable machine learning, deep learning, and intelligent algorithms that lead to more interoperable solutions. These solutions can make effective decisions for emerging medical data-driven requirements. Accessibility of data resources gives scope for health monitoring.

However, the current challenge is the data aggregated from multiple sensors for decisions. There are also challenges in terms of data blending in health monitoring. Fusion is a multi-domain developing field. It is mainly categorized as contextual information, observational data, complex health data, and learned knowledge. Health data fusion systems are providing dynamically changing situations by integrating sensors, outcomes, knowledge bases, databases, user mission, and contextual information.

This Special Issue aims to bring together original research and review articles reporting novel methodologies, theories, technologies, techniques, and solutions for big medical data analytics in healthcare systems. This Special Issue also hopes to address these topics across multiple abstraction levels, ranging from mathematical models, provisioning of services, optimization, short-or-long-range health informatics, and interfaces to specific implementation approaches. Submissions discussing the most important and relevant advances to overcome the challenges related to complex data analytics and processing via IoMT are welcome. Particularly, authors are encouraged to submit research considering current and potential applications for big complex healthcare systems.

Potential topics include but are not limited to the following:

• IoMT-enabled big medical data analytics for smart healthcare
• Computational complexity for IoMT-enabled health data analytics
• Medical imaging and signal processing and precision medicine for IoMT
• Cyber-physical systems and blockchain for complex health data
• Cloud computing for complex health data
• Context-aware security and privacy for medical data
• Large-scale data analytics tools and technologies for healthcare
• Complex biomedical complex data handling over IoMT
• Machine learning and deep learning approaches in IoMT-enabled health data analytics
• Intelligent decision making for big medical data analytics
• Data-driven mathematical modelling for complex health data analytics
• Modelling, simulation, and analysis for the resilience of complex health systems