Universidade Presbiteriana Mackenzie, PPGEEC, São Paulo, SP, Brazil.
Universidade de São Paulo, Escola Politécnica, São Paulo, SP, Brazil.
Math Biosci Eng. 2022 Mar 25;19(6):5380-5392. doi: 10.3934/mbe.2022252.
Bereavement exclusion (BE) is a criterion for excluding the diagnosis of major depressive disorder (MDD). Simplistically, this criterion states that an individual who reports MDD symptoms should not be diagnosed as suffering from this mental illness, if such an individual is grieving a sorrowful loss. BE was introduced in 1980 to avoid confusing MDD with normal grief, because several cognitive and physical symptoms of grief and depression can look similar. However, in 2013, BE was removed from the MDD diagnosis guidelines. Here, this controversial topic is computationally investigated. A virtual population is generated according to the Brazilian data of death rate and MDD prevalence and its five kinds of individuals are clustered by using a Kohonen's self-organizing map (SOM). In addition, by examining the current guidelines for diagnosing MDD from an analytical perspective, a slight modification is proposed. With this modification, an adequate clustering is achieved by the SOM neural network. Therefore, for mathematical consistency, unbalanced scores should be assigned to the items composing the MDD diagnostic criteria. With the proposed criteria, the co-occurrence of normal grief and MDD can also be satisfactorily clustered.
丧亲排除 (BE) 是排除重度抑郁障碍 (MDD) 诊断的标准之一。简单来说，这一标准指出，如果个体正在经历悲痛的丧失，那么报告 MDD 症状的个体不应被诊断为患有这种精神疾病。BE 于 1980 年被引入，以避免将 MDD 与正常悲伤混淆，因为悲伤和抑郁的一些认知和身体症状看起来相似。然而，在 2013 年，BE 从 MDD 诊断指南中被删除。在这里，对这一有争议的话题进行了计算研究。根据巴西的死亡率和 MDD 患病率数据，生成了一个虚拟人群，并使用科恩的自组织映射 (SOM) 对其五种类型的个体进行聚类。此外，通过从分析角度检查目前的 MDD 诊断指南，提出了一个微小的修改建议。通过这个修改，SOM 神经网络实现了适当的聚类。因此，为了保持数学一致性，应给构成 MDD 诊断标准的项目分配不平衡的分数。通过使用提出的标准，也可以令人满意地对正常悲伤和 MDD 的同时出现进行聚类。
