With the developments in technology, the number of Internet of Things (IoT) devices for biomedical and healthcare systems are increasing significantly and have produced enormous quantities of data through non-invasive measurement technologies and wearable sensors. The goal of biomedical informatics techniques is to extract meaningful knowledge and insights from current data rather than to acquire more data. In terms of clinical process execution, massive data is being gathered in the form of event data by systems such as electronic health record systems. Event data is an important source of information for analyzing and improving clinical processes.

In recent years, rapidly spreading disease has had a huge impact on society, both economically and politically. The development of quick and low-cost diagnostic tools is the major method needed to halt the spread of these diseases. This is also essential for other disorders that require quick and efficient diagnostic methods. However, big data cannot easily be analyzed by typical data processing algorithms and applications. With the support of sensor intelligence, the decision-making process and early illness detection can be improved. Therefore, scalable and collaborative machine learning algorithms in medical sensors are required for the acquisition, processing, and analysis of data in healthcare environments.

For this Special Issue, we encourage contributions on both theoretical and practical aspects of the application of IoT in medical sensors. This Special Issue provides an excellent opportunity for interdisciplinary scholars to propose novel informatics strategies for process mining in smart health care systems. We welcome original research and review papers.

Potential topics include but are not limited to the following:

• Analysis of biomedical signals and images
• Big data analytics for healthcare sensors
• Cloud/fog/edge computing and big medical data collected by real sensors
• Data-driven process recommendations in health care
• Data-driven simulation and optimization of health care processes
• Real-time decision making, prognosis, and diagnosis
• Smart sensors for IoT
• Statistical analysis, machine learning, and deep learning for biomedical signals
• AI, AIoT including local and edge AI for smart health applications