Abstract
The healthcare system is facing very important challenges in order to improve the whole system performance. Different communities are interested in this subject from different perspectives ranging from technical issues to organizational aspects. An important aspect of this research area is to consider social network data within the system especially because of the rapid and growing development of social networks. It can be general social networks, like Facebook or twitter but also others dedicated as PatientsLikeMe. This social network proliferation generates complex problems and locks when we want to take into account the resulting large amounts of data, created continuously, within the healthcare system. We call these data “social data”. The aim of this work is to demonstrate that is possible and feasible to build promising alternatives of the traditional healthcare system to improve the quality of services and reduce cost. In our opinion, taking into account “social data” can provide efficient healthcare decisional support systems to help healthcare operators to make optimal and efficient decisions in dynamic and complex environments. Our approach involves data extraction from multiple social networks, data aggregation, and the development of a semantic model in order to answer high-level users’ queries. In addition, we show how an analytical tool can help operators to understand data. Lastly, we present a model of machine learning which aims to detect the Sentiments of users expressed toward a given medication and the “TOP TRENDING” of care and treatments used for a given disease.
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El Hajjami, S., Berrada, M., Fhiyil, S. (2020). Healthcare Social Data Platform Based on Linked Data and Machine Learning. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_28
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DOI: https://doi.org/10.1007/978-981-15-0947-6_28
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