Introduction
Big Data, as well as Computational Intelligence, may be used to create innovative applications with value for both science and business. Through the use of sophisticated Big Data analytics algorithms and individual and population-based profiling, personalized health services may provide patients and physicians with optimal health information and recommendations.
Discussion
To provide personalized care and therapy intervention recommendations, it is crucial to have a deeper understanding of a person’s or a group’s healthcare requirements. Additionally, this encompasses fitness, lifestyle, and wellbeing monitoring based on personalized preferences and objectives that may be utilized to encourage behavior modification related to nutrition, exercise, and stress reduction, as well as to promote good health. Due to lower healthcare expenses, monitoring and providing these services through personal, cloud-based technology would enable people better manage their own health and way of life.
An example of an innovative personalized health application that has been recently developed includes a mobile healthcare framework based on a cloud-based collaborative network. Mukherjee et al. (2021) present a system that uses edge and fog devices with the purpose of monitoring a person’s health and their normal parameters and informing people if they experience stress or other unhealthy conditions. Should these be seen as critical, the user’s mobility data can be analyzed to determine the optimal route toward a nearby health center. The use of cloud services also helps save energy on the device, as it is more energy-efficient than engaging its storage service. The innovation by Mukherjee et al. (2021), thus, presents how personalized health data and analytics can be used to improve people’s everyday lives. At the same time, the method chosen to be applied in this health application raises concerns about personal data security.
Conclusion
Understanding user information needs in healthcare as well as the rest of the application domains. These include optimized visualization of real-time data, such as air traffic information and stock market information, which can be based on context and task-oriented presentation of retrieved content to reduce the cognitive burden on users. With the device reviewed in this paper, the cognitive burden is minimal, with the app attracting the user’s attention only when they are deemed to be in danger.
Reference
Mukherjee, A., Ghosh, S., Behere, A., Ghosh, S. K., & Buyya, R. (2021). Internet of Health Things (IoHT) for personalized health care using integrated edge-fog-cloud network. Journal of Ambient Intelligence and Humanized Computing, 12(1), 943-959. Web.