Towards Multi-Level Modelling and Monitoring of Real-time Personalised Health Conditions

This paper presents an example-based demonstration of our initial results on the modelling of health conditions to support the control and monitoring of (clinically) personalized healthcare services. The main goal of this work is to model health conditions at the interface, mechanical, biological, and environmental levels to support rigorous and reliable personalised healthcare services for unreliable IoT-based Healthcare 5.0 using different abstractions like rules, processes, and rates. Current approaches support either fine-grained (i.e., at DNA or protein level considering the effect of health conditions on cellular components, biological processes, and molecular functions) or coarse-grained (i.e., only parameters check ignoring the internal details and causes) modelling of various health conditions that hindered their usability to automatically control and monitor healthcare conditions in real-time due to lack of missing dependent information at different levels of health condition. Modelling health conditions is a challenging task because health condition is a complex process that involves various inter-dependent cyber, physical, mechanical, and biological sub-processes. We have developed a technique for modelling health conditions at multiple levels that supports various cyber, physical, and biological characteristics of health-condition and operate at different levels of abstraction including both coarse-grained and fine-grained levels that are related. We demonstrate the modelling technique through its application to a typical healthcare condition that includes Diabetes and Heart conditions