AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
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.
