MentalRiskES is a novel challenge that proposes to solve problems related to
early risk detection for the Spanish language. The objective is to detect, as
soon as possible, Telegram users who show signs of mental disorders considering
different tasks. Task 1 involved the users' detection of eating disorders, Task
2 focused on depression detection, and Task 3 aimed at detecting an unknown
disorder. These tasks were divided into subtasks, each one defining a
resolution approach. Our research group participated in subtask A for Tasks 1
and 2: a binary classification problem that evaluated whether the users were
positive or negative. To solve these tasks, we proposed models based on
Transformers followed by a decision policy according to criteria defined by an
early detection framework. One of the models presented an extended vocabulary
with important words for each task to be solved. In addition, we applied a
decision policy based on the history of predictions that the model performs
during user evaluation. For Tasks 1 and 2, we obtained the second-best
performance according to rankings based on classification and latency,
demonstrating the effectiveness and consistency of our approaches for solving
early detection problems in the Spanish language.

MentalRiskES 是一个新颖的挑战，提出解决与西班牙语相关的早期风险检测问题，在不同任务中尽早检测出显示出心理障碍迹象的 Telegram 用户。

西班牙语中早期发现抑郁症与饮食失调：UNSL 参加 MentalRiskES 2023

Early Detection of Depression and Eating Disorders in Spanish: UNSL at  MentalRiskES 2023

Depression is a prominent health challenge to the world, and early risk
detection (ERD) of depression from online posts can be a promising technique
for combating the threat. Early depression detection faces the challenge of
efficiently tackling streaming data, balancing the tradeoff between timeliness,
accuracy and explainability. To tackle these challenges, we propose a
psychiatric scale guided risky post screening method that can capture risky
posts related to the dimensions defined in clinical depression scales, and
providing interpretable diagnostic basis. A Hierarchical Attentional Network
equipped with BERT (HAN-BERT) is proposed to further advance explainable
predictions. For ERD, we propose an online algorithm based on an evolving queue
of risky posts that can significantly reduce the number of model inferences to
boost efficiency. Experiments show that our method outperforms the competitive
feature-based and neural models under conventional depression detection
settings, and achieves simultaneous improvement in both efficacy and efficiency
for ERD.

本文提出了一种基于心理量表指导的风险帖子筛选方法，采用具有可解释性的 Hierarchical Attentional Network 和 BERT 模型进行预测，以及一种基于不断更新的风险帖子队列的在线算法，用于快速高效地检测抑郁风险。实验证明，该方法在传统抑郁预测中胜过竞争对手的基于特征和神经网络的模型，并在 ERD 的效率和准确性方面实现了同时改进。