Psychodynamic conflicts are persistent, often unconscious themes that shape a person's behaviour and experiences. Accurate diagnosis of psychodynamic conflicts is crucial for effective patient treatment and is commonly done via long, manually scored semi-structured interviews. Existing automated solutions for psychiatric diagnosis tend to focus on the recognition of broad disorder categories such as depression, and it is unclear to what extent psychodynamic conflicts which even the patient themselves may not have conscious access to could be automatically recognised from conversation. In this paper, we propose AutoPsyC, the first method for recognising the presence and significance of psychodynamic conflicts from full-length Operationalized Psychodynamic Diagnostics (OPD) interviews using Large Language Models (LLMs). Our approach combines recent advances in parameter-efficient fine-tuning and Retrieval-Augmented Generation (RAG) with a summarisation strategy to effectively process entire 90 minute long conversations. In evaluations on a dataset of 141 diagnostic interviews we show that AutoPsyC consistently outperforms all baselines and ablation conditions on the recognition of four highly relevant psychodynamic conflicts.

翻译：心理动力学冲突是持久存在、通常无意识的主题，它塑造了个体的行为与体验。准确诊断心理动力学冲突对于有效治疗患者至关重要，目前通常通过耗时冗长、需人工评分的半结构化访谈完成。现有的精神疾病诊断自动化方案多侧重于识别广泛障碍类别（如抑郁症），而对于患者自身可能都无法意识到的心理动力学冲突，能否通过对话实现自动识别尚不明确。本文提出AutoPsyC，这是首个利用大语言模型从完整的操作性心理动力学诊断访谈中识别心理动力学冲突的存在及其显著性的方法。我们的方法结合了参数高效微调与检索增强生成技术的最新进展，并采用摘要生成策略以有效处理长达90分钟的完整对话。在包含141份诊断访谈的数据集评估中，我们证明AutoPsyC在识别四种高度相关的心理动力学冲突任务上，始终优于所有基线模型及消融实验条件。