Nowadays, Healthcare Social Networks (HSNs) offer the possibility 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 along with their bad interpretation. On one hand doctors and practitioners want to promote the exchange of information among patients about a specific disease, but on the other hand they do not have enough time to read patients’ posts and moderate them when required. In this paper, we investigate and compare different supervised learning classifiers that we adopted for the classification of critical patients’ posts who can trigger the intervention of the medical personnel. In particular, by considering different Bayesian, Linear and Support Vector Machine (SVM) classifiers we analyse their accuracy considering different n-grams datasets preparation approaches in order to identify the best approach for the identification of critical patients’ posts in a Healthcare Social Network.

Investigating classification supervised learning approaches for the identification of critical patients’ posts in a healthcare social network

Carnevale, Lorenzo
Primo
;
Celesti, Antonio
Secondo
;
Fiumara, Giacomo;Galletta, Antonino
Penultimo
;
Villari, Massimo
Ultimo
2020-01-01

Abstract

Nowadays, Healthcare Social Networks (HSNs) offer the possibility 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 along with their bad interpretation. On one hand doctors and practitioners want to promote the exchange of information among patients about a specific disease, but on the other hand they do not have enough time to read patients’ posts and moderate them when required. In this paper, we investigate and compare different supervised learning classifiers that we adopted for the classification of critical patients’ posts who can trigger the intervention of the medical personnel. In particular, by considering different Bayesian, Linear and Support Vector Machine (SVM) classifiers we analyse their accuracy considering different n-grams datasets preparation approaches in order to identify the best approach for the identification of critical patients’ posts in a Healthcare Social Network.
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3150656
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