Abstract
Personalized healthcare applications are designed with the help of handheld devices for providing instantaneous monitoring and diagnosis. Modern healthcare applications are designed with recommendation systems for providing diagnosis assistance for the users. This article introduces a novel situation-aware recommendation system for improving the reliability of personal healthcare applications. This recommendation system relies on recurrent neural learning for identifying the situation based on different physiological vitals. The learning method identifies a similar cause from the previous history and augments a recommendation from the connected healthcare server. The process of the situation is understood for providing equal and instantaneous recommendations for the patients. In this learning, the situation’s training and the previous cases of occurrence are performed concurrently for identifying matching instances, reducing the complexity. The proposed system’s performance is verified using the metrics matching ratio, complexity, recommendation delay, and accuracy.
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Acknowledgments
The authors would like to extend their gratitude to King Saud University (Riyadh, Saudi Arabia) for funding this research through Researchers Supporting Project number (RSP-2020/260).
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Saad, A., Fouad, H. & Mohamed, A.A. Situation-aware recommendation system for personalized healthcare applications. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02927-1
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DOI: https://doi.org/10.1007/s12652-021-02927-1