Social interaction allows to support the disease management by creating online spaces where patients can interact with clinicians, and share experiences with other patients. Therefore, promoting targeed communication in online social spaces is a mean to group patients around shared goals, offer emotional support, and finally engage patients in their healthcare decision-making process. In this paper, we approach the argument from a theoretical perspective: we design an optimization problem aimed to encourage the creation of (induced) sub-networks of patients which, being recently diagnosed, wish to deepen the knowledge about their medical treatment with some other similar profiled patients, which have already been followed up by specific (even alternative) care centers. In particular, due to the computational hardness of the proposed problem, we provide approximated solutions based on distributed heuristics (i.e., Genetic Algorithms). Results are given for simulated data using Erd ̈os-R ́enyi random graphs.
Zoppis, I., Dondi, R., Manzoni, S., Mauri, G. (2019). Patient Engagement: Theoretical and Heuristic Approaches for Supporting the Clinical Practice.. In Proceedings of the Fourth Italian Workshop on Artificial Intelligence for Ambient Assisted Living 2018 co-located with 17th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2018) (pp.51-66).
Patient Engagement: Theoretical and Heuristic Approaches for Supporting the Clinical Practice.
Zoppis, I
;Dondi, R;Manzoni, S;Mauri, G
2019
Abstract
Social interaction allows to support the disease management by creating online spaces where patients can interact with clinicians, and share experiences with other patients. Therefore, promoting targeed communication in online social spaces is a mean to group patients around shared goals, offer emotional support, and finally engage patients in their healthcare decision-making process. In this paper, we approach the argument from a theoretical perspective: we design an optimization problem aimed to encourage the creation of (induced) sub-networks of patients which, being recently diagnosed, wish to deepen the knowledge about their medical treatment with some other similar profiled patients, which have already been followed up by specific (even alternative) care centers. In particular, due to the computational hardness of the proposed problem, we provide approximated solutions based on distributed heuristics (i.e., Genetic Algorithms). Results are given for simulated data using Erd ̈os-R ́enyi random graphs.File | Dimensione | Formato | |
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