We employ a Large Language Model (LLM) to convert unstructured psychological
interviews into structured questionnaires spanning various psychiatric and
personality domains. The LLM is prompted to answer these questionnaires by
impersonating the interviewee. The obtained answers are coded as features,
which are used to predict standardized psychiatric measures of depression
(PHQ-8) and PTSD (PCL-C), using a Random Forest regressor. Our approach is
shown to enhance diagnostic accuracy compared to multiple baselines. It thus
establishes a novel framework for interpreting unstructured psychological
interviews, bridging the gap between narrative-driven and data-driven
approaches for mental health assessment.

我们利用大型语言模型将非结构化的心理访谈转化为涵盖各种精神疾病和人格领域的结构化问卷，通过模拟被访者的方式让语言模型回答这些问卷。得到的答案被编码为特征，并使用随机森林回归器预测抑郁症（PHQ-8）和创伤后应激障碍（PCL-C）的标准化精神健康测量值，比多个基准模型表现出更高的诊断准确性。因此，我们的研究提出了一种新的框架来解释非结构化的心理访谈，缩小叙事驱动和数据驱动方法在心理健康评估中的差距。

自动精神疾病评估中的 LLM 问卷填写

LLM Questionnaire Completion for Automatic Psychiatric Assessment

Clinical NLP tasks such as mental health assessment from text, must take
social constraints into account - the performance maximization must be
constrained by the utmost importance of guaranteeing privacy of user data.
Consumer protection regulations, such as GDPR, generally handle privacy by
restricting data availability, such as requiring to limit user data to 'what is
necessary' for a given purpose. In this work, we reason that providing stricter
formal privacy guarantees, while increasing the volume of user data in the
model, in most cases increases benefit for all parties involved, especially for
the user. We demonstrate our arguments on two existing suicide risk assessment
datasets of Twitter and Reddit posts. We present the first analysis juxtaposing
user history length and differential privacy budgets and elaborate how modeling
additional user context enables utility preservation while maintaining
acceptable user privacy guarantees.

本文探讨在严格保护用户隐私的前提下，提高数据量以增进所有相关方的利益的方法， 并通过对 Twitter 和 Reddit 上两个现有问题的分析，展示了如何在保证用户隐私的同时，通过建模更多的用户上下文，实现效用的保存。