Depressive disorders constitute a severe public health issue worldwide.
However, public health systems have limited capacity for case detection and
diagnosis. In this regard, the widespread use of social media has opened up a
way to access public information on a large scale. Computational methods can
serve as support tools for rapid screening by exploiting this user-generated
social media content. This paper presents an efficient semantic pipeline to
study depression severity in individuals based on their social media writings.
We select test user sentences for producing semantic rankings over an index of
representative training sentences corresponding to depressive symptoms and
severity levels. Then, we use the sentences from those results as evidence for
predicting users' symptom severity. For that, we explore different aggregation
methods to answer one of four Beck Depression Inventory (BDI) options per
symptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\%
improvement over state of the art in terms of measuring depression severity.

本文提出了一个有效的语义管道，基于社交媒体的写作，研究个体的抑郁症严重程度。通过计算方法，利用用户生成的社交媒体内容，本文探讨了不同聚合方法以准确预测用户症状严重程度，并在 Reddit 的两个基准测试中取得了 30％的提高。

用于抑郁症程度估计的语义相似性模型

Semantic Similarity Models for Depression Severity Estimation

We present our preliminary work to determine if patient's vocal acoustic,
linguistic, and facial patterns could predict clinical ratings of depression
severity, namely Patient Health Questionnaire depression scale (PHQ-8). We
proposed a multi modal fusion model that combines three different modalities:
audio, video , and text features. By training over AVEC 2017 data set, our
proposed model outperforms each single modality prediction model, and surpasses
the data set baseline with ice margin.

本研究旨在探讨通过使用多模态融合模型，利用患者声音、视频、文本等特征预测临床评估中的抑郁症状，PHQ-8，该模型在训练 AVEC 2017 数据集时表现优于每个单一模态的预测模型，并超越数据集的基线水平。