Authors:
Giacomo Fiumara
1
;
Antonio Celesti
2
;
Antonino Galletta
3
;
Lorenzo Carnevale
3
and
Massimo Villari
3
Affiliations:
1
University of Messina, Italy
;
2
University of Messina and Alma Digit S.R.L. Research Labs, Italy
;
3
University of Messina and IRCCS Centro Neurolesi "Bonino Pulejo", Italy
Keyword(s):
Healthcare, Social Network, Artificial Intelligence, Machine Learning.
Abstract:
Nowadays, the possibility of using social media in the healthcare field is attracting the attention of clinical
professionals and of the whole healthcare industry. In this panorama, many Healthcare Social Networking
(HSN) platforms are emerging with the purpose to enhance patient care and education. However, they also
present potential risks for patients due to the possible distribution of poor-quality or wrong information. On
one hand doctors want to promote the exchange of information among patients about a specific disease, but on
the other hand they do not have the time to read patients’ posts and moderate them when required. In this paper,
we propose an Artificial Intelligence (AI) approach based on a combination of stemming, lemmatization and
Machine Learnign (ML) algorithms that allows to automatically analyse the patients’ posts of a HSN platform
and identify possible critical issues so as to enable doctors to intervene when required. In particular, after a
discussion of adva
ntages and disadvantages of using a HSN platform, we discuss in detail an architecure that
allows to analyse big data consisting of patients’ posts. In the end, real case studies are discussed highlighting
future challenges.
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