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Knowledge Fusion Based on Cloud Computing Environment for Long-Term Care

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  • Kai-Xiang Zhuang

    (National Formosa University, Taiwan)

  • I-Ching Hsu

    (National Formosa University, Taiwan)

Abstract

Globally, aging is now a societal trend and challenge in many developed and developing countries. A key medical strategy that a fast-paced aging society must consider is the provision of quality long-term care (LTC) services. Even so, the lack of LTC caregivers is a persistent global problem. Herein, attention is called to the increasing need for identifying appropriate LTC caregivers and delivering client-specific LTC services to the elderly via emerging and integrative technologies. This paper argues for the use of an intelligent cloud computing long-term care platform (ICCLCP) that integrates statistical analysis, machine learning, and Semantic Web technologies into a cloud-computing environment to facilitate LTC services delivery. The Term frequency-inverse document frequency is a numerical statistic adopted to automatically assess the professionalism of each LTC caregiver's services. The machine learning method adopts naïve Bayes classifier to estimate the LTC services needed for the elderly. These two items of LTC information are integrated with the Semantic Web to provide an intelligent LTC framework. The deployed ICCLCP will then aid the elderly in the recommendation of LTC caregivers, thereby making the best use of available resources for LTC services.

Suggested Citation

  • Kai-Xiang Zhuang & I-Ching Hsu, 2020. "Knowledge Fusion Based on Cloud Computing Environment for Long-Term Care," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 15(4), pages 38-55, October.
    • Handle: RePEc:igg:jhisi0:v:15:y:2020:i:4:p:38-55
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    1. Shalini Gambhir & Sanjay Kumar Malik & Yugal Kumar, 2018. "The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 13(3), pages 1-19, July.
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