Major Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person’s day-to-day activity. In addition, MDD affects one’s linguistic footprint, which is reflected by subtle changes in speech production. This allows us to use natural language processing (NLP) techniques to build a neural classifier to detect depression from speech transcripts. Typically, current NLP systems discriminate only between the depressed and non-depressed states. This approach, however, disregards the complexity of the clinical picture of depression, as different people with MDD can suffer from different sets of depression symptoms. Therefore, predicting individual symptoms can provide more fine-grained information about a person’s condition. In this work, we look at the depression classification problem through the prism of the symptom network analysis approach, which shifts attention from a categorical analysis of depression towards a personalized analysis of symptom profiles. For that purpose, we trained a multi-target hierarchical regression model to predict individual depression symptoms from patient–psychiatrist interview transcripts from the DAIC-WOZ corpus. Our model achieved results on par with state-of-the-art models on both binary diagnostic classification and depression severity prediction while at the same time providing a more fine-grained overview of individual symptoms for each person. The model achieved a mean absolute error (MAE) from 0.438 to 0.830 on eight depression symptoms and showed state-of-the-art results in binary depression estimation (73.9 macro-F1) and total depression score prediction (3.78 MAE). Moreover, the model produced a symptom correlation graph that is structurally identical to the real one. The proposed symptom-based approach provides more in-depth information about the depressive condition by focusing on the individual symptoms rather than a general binary diagnosis.

中文翻译：

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通过症状预测实现基于文本的自动抑郁症评估

重度抑郁症 (MDD) 是影响一个人日常活动的最常见和共病的精神障碍之一。此外，MDD 影响一个人的语言足迹，这反映在言语产生的细微变化上。这使我们能够使用自然语言处理 (NLP) 技术来构建神经分类器，以从语音记录中检测抑郁症。通常，当前的 NLP 系统仅区分抑郁和非抑郁状态。然而，这种方法忽视了抑郁症临床表现的复杂性，因为不同的重度抑郁症患者可能会出现不同的抑郁症症状。因此，预测个体症状可以提供有关个人状况的更细粒度的信息。在这项工作中，我们通过症状网络分析方法的棱镜来看待抑郁症分类问题，这将注意力从抑郁症的分类分析转移到症状概况的个性化分析。为此，我们训练了一个多目标层次回归模型来预测来自 DAIC-WOZ 语料库的患者-精神病医生访谈记录的个体抑郁症状。我们的模型在二元诊断分类和抑郁严重程度预测方面取得了与最先进模型相当的结果，同时为每个人提供了更细粒度的个体症状概览。该模型在八种抑郁症状上实现了从 0.438 到 0.830 的平均绝对误差 (MAE)，并在二元抑郁估计中显示了最先进的结果 (73. 9 macro-F1) 和总抑郁评分预测 (3.78 MAE)。此外，该模型生成的症状相关图在结构上与真实图完全相同。所提出的基于症状的方法通过关注个体症状而不是一般的二元诊断来提供有关抑郁症的更深入的信息。