The events of the past 2 years related to the pandemic have shown that it is
increasingly important to find new tools to help mental health experts in
diagnosing mood disorders. Leaving aside the longcovid cognitive (e.g.,
difficulty in concentration) and bodily (e.g., loss of smell) effects, the
short-term covid effects on mental health were a significant increase in
anxiety and depressive symptoms. The aim of this study is to use a new tool,
the online handwriting and drawing analysis, to discriminate between healthy
individuals and depressed patients. To this aim, patients with clinical
depression (n = 14), individuals with high sub-clinical (diagnosed by a test
rather than a doctor) depressive traits (n = 15) and healthy individuals (n =
20) were recruited and asked to perform four online drawing /handwriting tasks
using a digitizing tablet and a special writing device. From the raw collected
online data, seventeen drawing/writing features (categorized into five
categories) were extracted, and compared among the three groups of the involved
participants, through ANOVA repeated measures analyses. Results shows that Time
features are more effective in discriminating between healthy and participants
with sub-clinical depressive characteristics. On the other hand, Ductus and
Pressure features are more effective in discriminating between clinical
depressed and healthy participants.

本研究使用在线手写和绘画分析工具区分健康个体和抑郁症患者的差异，结果表明时间特征对于区分亚临床抑郁症和健康组更具有效性，而导线和压力特征对于区分临床抑郁症和健康组更为有效。

手写和绘画用于抑郁症检测：初步研究

Handwriting and Drawing for Depression Detection: A Preliminary Study

We describe the development of a model to detect user-level clinical
depression based on a user's temporal social media posts. Our model uses a
Depression Symptoms Detection (DSD) classifier, which is trained on the largest
existing samples of clinician annotated tweets for clinical depression
symptoms. We subsequently use our DSD model to extract clinically relevant
features, e.g., depression scores and their consequent temporal patterns, as
well as user posting activity patterns, e.g., quantifying their ``no activity''
or ``silence.'' Furthermore, to evaluate the efficacy of these extracted
features, we create three kinds of datasets including a test dataset, from two
existing well-known benchmark datasets for user-level depression detection. We
then provide accuracy measures based on single features, baseline features and
feature ablation tests, at several different levels of temporal granularity.
The relevant data distributions and clinical depression detection related
settings can be exploited to draw a complete picture of the impact of different
features across our created datasets. Finally, we show that, in general, only
semantic oriented representation models perform well. However, clinical
features may enhance overall performance provided that the training and testing
distribution is similar, and there is more data in a user's timeline. The
consequence is that the predictive capability of depression scores increase
significantly while used in a more sensitive clinical depression detection
settings.

本文研究使用 DSD 模型和社交媒体帖子来检测用户级别的临床抑郁，通过提取临床相关特征和用户发布活动的时间模式，最终提高了临床抑郁的检测准确性。

通过社交媒体文本深度建模临床抑郁症

Deep Temporal Modelling of Clinical Depression through Social Media Text

A fundamental component of user-level social media language based clinical
depression modelling is depression symptoms detection (DSD). Unfortunately,
there does not exist any DSD dataset that reflects both the clinical insights
and the distribution of depression symptoms from the samples of self-disclosed
depressed population. In our work, we describe a Semi-supervised Learning (SSL)
framework which uses an initial supervised learning model that leverages 1) a
state-of-the-art large mental health forum text pre-trained language model
further fine-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot
learning model for DSD, and couples them together to harvest depression
symptoms related samples from our large self-curated Depression Tweets
Repository (DTR). Our clinician annotated dataset is the largest of its kind.
Furthermore, DTR is created from the samples of tweets in self-disclosed
depressed users Twitter timeline from two datasets, including one of the
largest benchmark datasets for user-level depression detection from Twitter.
This further helps preserve the depression symptoms distribution of
self-disclosed Twitter users tweets. Subsequently, we iteratively retrain our
initial DSD model with the harvested data. We discuss the stopping criteria and
limitations of this SSL process, and elaborate the underlying constructs which
play a vital role in the overall SSL process. We show that we can produce a
final dataset which is the largest of its kind. Furthermore, a DSD and a
Depression Post Detection (DPD) model trained on it achieves significantly
better accuracy than their initial version.

使用半监督学习和大规模文本预训练语言模型，基于社交媒体语言数据，构建了最大的自述抑郁症状识别数据集，并训练出表现显著提高的抑郁症状检测模型。

基于社交媒体文本的抑郁症状建模：一种半监督学习方法

Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach

With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.

通过对 Twitter 上患有抑郁症状用户的帖子的分析，本研究探索了抑郁症潜在的可靠检测方法，使用半监督统计模型评估了这些症状的持续时间以及它们在 Twitter 上的表达方式与 PHQ-9 医学发现的一致性，获得了最高 68% 的准确性和 72% 的精确度。