Distributed Heuristics for Optimizing Cohesive Groups: A Support for Clinical Patient Engagement in Social Network Analysis

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 targeted communication in online social spaces is a means 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 Erdos-Renyi random graphs.