In recent years, the Internet of Health Informatics Things (IoHIT) has evolved as a great support to people in relation to out-of-hospital care. The IoHIT is designed mainly to sense the individual’s health status while they are in their own routine life. With the aid of scientific algorithms, a prelearned intelligent system can be developed for analysing the health data of the individual in real time and reporting them to either a clinician or the caretaker, so that any emergency situations can be attended in the earliest time.

Thus, IoHIT brings the need of big data computing and data security. Therefore, there is a necessity for providing stable, efficient, and scalable intelligent scientific computing algorithms that lead to additional sophisticated solutions to make decisions in developing the IoHIT.

This Special Issue is based on developing technical improvements in the process of designing intelligent systems using scientific computing techniques for IoHIT. This includes deep learning and machine learning techniques for multimodal biomedical data processing. Furthermore, the Special Issue invites original research and review articles to highlight the challenges of developing and proposing new ideas regarding out-of-hospital dedicated systems directions.

Potential topics include but are not limited to the following:

• Scientific computing for biomedical big data analysis
• Scientific computing in IoHIT data encryption and security
• Data storage scheme for IoHIT for heavy computing
• Scientific computing for cloud security for IoHIT
• IoHIT cloud technique-based and intelligent computing systems
• Deep learning computation for health informatics
• Scientific computing for optimised wearable IoHIT devices
• Scientific computing for data mining in IoHIT
• Machine learning computing for decision support systems in IoHIT