Social media can directly support disease management by creating online spaces where patients can interact with clinicians, and share experiences with other patients. Nevertheless, much more work remains to be carried out for providing and sharing an optimized information content. In this paper we formulate, from a theoretical perspective, an optimization problem aimed to encourage the creation of a sub-network of patients which, being recently diagnosed, wish to deepen their knowledge about their pathologies with some other patients, whose clinical profile turn to be similar, and have already been followed up within specific, even alternative, care centers. We will focus on the hardness of the proposed problem and provide a Genetic Algorithm (GAbased) approach to seek faster approximated solutions.
Mauri, G., Sicurello, F., Castelnuovo, G., Santoro, E., Dondi, R., Zoppis, I. (2018). Optimizing Social Interaction - A Computational Approach to Support Patient Engagement. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF 2018) (pp.651-657). Setubal : SCITEPRESS – Science and Technology Publications, Lda. [10.5220/0006730606510657].
Optimizing Social Interaction - A Computational Approach to Support Patient Engagement
Mauri, G;SICURELLO, FRANCESCO;Zoppis, I
2018
Abstract
Social media can directly support disease management by creating online spaces where patients can interact with clinicians, and share experiences with other patients. Nevertheless, much more work remains to be carried out for providing and sharing an optimized information content. In this paper we formulate, from a theoretical perspective, an optimization problem aimed to encourage the creation of a sub-network of patients which, being recently diagnosed, wish to deepen their knowledge about their pathologies with some other patients, whose clinical profile turn to be similar, and have already been followed up within specific, even alternative, care centers. We will focus on the hardness of the proposed problem and provide a Genetic Algorithm (GAbased) approach to seek faster approximated solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.