基于潜类别分析模型的抑郁症分型的研究进展
Progress in researches on latent class analysis based subtyping of depression

抑郁症具有高度临床异质性，根据症状学特征对抑郁症进行同质性分型可能有助于更具针对性地选择治疗决策，进行疗效评估和预后判断。潜类别分析可通过对潜变量的模型化分析对异质性人群进行同质性分类，有助于提高疾病临床分型的准确性，在抑郁症分型研究中逐渐获得广泛应用。文章对抑郁症临床描述性分型的现状及潜类别分析在疾病分型中的应用进行综述。
：Depression is a highly heterogeneous syndrome. Homogeneous subtypes according to symptomatology of illness may contribute to development of individualized treatment, assessment on outcomes and prognosis. Latent class analysis is a flexible statistical approach to determine classes with similar symptom profiles in a heterogeneous group, which has been widely used in data-driven subtyping of depression to increase accuracy of subtyping. This article reviewed existing symptom-based subtypes of depression and findings of researches on latent class analysis based illness subtyping