On Automatic Formation of Effective Therapy Groups in Social Networks

Group therapy is one of the major clinical interventions to help patients with mental disorders. However, finding an effective therapy group is very challenging, since there are three crucial criteria that should be carefully considered simultaneously: 1) avoidance of isolation and loneliness; 2) unfamiliarity of patients; and 3) size of the therapy group. Manual selection of therapy group members, which is the common approach for most psychiatrists and mental health professionals today, may incur human bias and is very time-consuming. Therefore, we propose a new research problem, namely, Unfamiliarity-Aware Therapy Group Selection with Noah's Ark Principle (UTNA) for automatic selections of therapy group members from the social network while addressing the three crucial criteria mentioned above. In this paper, we first analyze the NP-hardness and inapproximability of the UTNA problem. Then, we consider a special case of UTNA on trees, namely, Tree-based UTNA, and propose a linear-time dynamic programming algorithm to find the optimal solution. We then design an efficient algorithm, Effective Therapy Group Discovery, to select suitable members efficiently and effectively for the general UTNA problem. We invite 10 psychiatrists and clinical psychologists to conduct an expert study for validating our problem formulation, where all experts agree that our problem formulation is helpful for them, and the groups selected by our proposed approach are better than or similar to their manual configurations. In addition, we also conduct extensive experiments on three real data sets. Experimental results show that our proposed algorithms outperform other baselines in both efficiency and solution quality.