Patients with serious illnesses or injuries may decide to quit their medical treatment if they think paying the fees will put their families into destitution. Without treatment, it is likely that fatal outcomes will soon follow. We call this phenomenon “near-suicide”. This study attempted to explore this phenomenon by examining how the seriousness of the patient’s illness or injury and the subjective evaluation of the patient’s and family’s financial situation after paying treatment fees affect the final decision on the treatment process. Bayesian Mindsponge Framework (BMF) analytics were employed to analyze a dataset of 1042 Vietnamese patients. We found that the more serious the illnesses or injuries of patients were, the more likely they were to choose to quit treatment if they perceived that paying the treatment fees heavily affected their families’ financial status. Particularly, only one in four patients with the most serious health issues who thought that continuing the treatment would push themselves and their families into destitution would decide to continue the treatment. Considering the information-filtering mechanism using subjective cost–benefit judgments, these patients likely chose the financial well-being and future of their family members over their individual suffering and inevitable death. Our study also demonstrates that mindsponge-based reasoning and BMF analytics can be effective in designing and processing health data for studying extreme psychosocial phenomena. Moreover, we suggest that policymakers implement and adjust their policies (e.g., health insurance) following scientific evidence to mitigate patients’ likelihood of making “near-suicide” decisions and improve social equality in the healthcare system.

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The phase transition driven by electron correlations in a Chern insulator is investigated within the dynamical mean-field theory. The Chern insulator is described by the Haldane model and the electron correlations are incorporated by introducing the short-range interaction between the itinerant electrons and localized fermions. In the preservation of the inversion symmetry, the electron correlations drive the system from the Chern insulator to a renormalized pseudogap metal, and then to the topologically trivial Mott insulator. When the inversion symmetry is broken, a charge ordering and a nontrivial Chern topological invariant coexist

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Patients with serious illnesses or injuries may decide to quit their medical treatment if they think paying the fees will put their families into destitution. Without treatment, it is likely that fatal outcomes will soon follow. We call this phenomenon “near-suicide”. Research on suicide-related psychology often faces huge difficulties in collecting and processing data due to the extreme nature of such phenomena. Employing the Bayesian Mindsponge Framework (BMF) analytics on a dataset of 1042 Vietnamese patients, we found that the more serious the illnesses or injuries of patients are, the more likely they will choose to quit treatment if they perceive that paying the treatment fees heavily affect their families’ financial status. Particularly, only one in four patients with the most serious health issues who thought that continuing the treatment would push themselves and their families into destitution or bankruptcy would decide to continue the treatment. Considering the information filtering mechanism using subjective cost-benefit judgments, these patients likely choose the financial well-being and future of their family members over their individual suffering and inevitable death. Our study also demonstrates that mindsponge-based reasoning and BMF analytics can be effective in designing and processing health data for studying extreme psychosocial phenomena.