Applying Artificial Intelligence in Healthcare Social Networks to Identity Critical Issues in Patients’ Posts

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 advantages and disadvantages of using a HSN platform, we discuss in detail an architecture that allows to analyse big data consisting of patients’ posts. In the end, real case studies are discussed highlighting future challenges.