Using Social Media to Predict Children Disease Occurrence
Journal
Proceedings of 7th IADIS International Conference Information Systems, Spain
Date Issued
2014
Author(s)
Apostolova Trpkovska, Marika
Cico, Betim
Abstract
Delivering care in the future may focus on predicting health needs rather than waiting for disease to begin. So, the future
of health care may be in “predictive health” that emphasizes as much as possible prediction and as less as possible
diagnoses that are coming from these predictions. Nowadays, the researchers are mining the data provided in social
networks, aiming in prediction of diverse phenomena like social, political, medical, etc. We are focused on proposing a
model for predicting children general diseases. The prediction task of the health related issues of specific people from
noisy data is taken into consideration. We offer a model that can predict children general diseases with high percentage
precision and good semantic recall on the basis of special designed ontology and social ties with other people, as revealed
by their posts in social networks which is advised to be used by young mothers. This model is a part of the specially
designed social network where the patient medical data are not included concerning their privacy and sensitivity.
Through the social network we a giving the possibility to mothers to exchange their experiences through different social
services. And through the proposed disease prediction model we a giving the possibility to mothers to gain some
preliminary predicted diagnosis, if needed.
of health care may be in “predictive health” that emphasizes as much as possible prediction and as less as possible
diagnoses that are coming from these predictions. Nowadays, the researchers are mining the data provided in social
networks, aiming in prediction of diverse phenomena like social, political, medical, etc. We are focused on proposing a
model for predicting children general diseases. The prediction task of the health related issues of specific people from
noisy data is taken into consideration. We offer a model that can predict children general diseases with high percentage
precision and good semantic recall on the basis of special designed ontology and social ties with other people, as revealed
by their posts in social networks which is advised to be used by young mothers. This model is a part of the specially
designed social network where the patient medical data are not included concerning their privacy and sensitivity.
Through the social network we a giving the possibility to mothers to exchange their experiences through different social
services. And through the proposed disease prediction model we a giving the possibility to mothers to gain some
preliminary predicted diagnosis, if needed.
Subjects
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