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。