TalkDep: Clinically Grounded LLM Personas for Conversation-Centric Depression Screening
This addresses the problem of limited training resources for clinical professionals in mental health, offering a scalable solution for depression screening, though it is incremental as it builds on existing simulation methods.
The paper tackles the shortage of real training data for depression diagnosis by developing TalkDep, a clinician-in-the-loop pipeline using language models to generate clinically valid simulated patients, verified by professionals to improve diagnostic systems.
The increasing demand for mental health services has outpaced the availability of real training data to develop clinical professionals, leading to limited support for the diagnosis of depression. This shortage has motivated the development of simulated or virtual patients to assist in training and evaluation, but existing approaches often fail to generate clinically valid, natural, and diverse symptom presentations. In this work, we embrace the recent advanced language models as the backbone and propose a novel clinician-in-the-loop patient simulation pipeline, TalkDep, with access to diversified patient profiles to develop simulated patients. By conditioning the model on psychiatric diagnostic criteria, symptom severity scales, and contextual factors, our goal is to create authentic patient responses that can better support diagnostic model training and evaluation. We verify the reliability of these simulated patients with thorough assessments conducted by clinical professionals. The availability of validated simulated patients offers a scalable and adaptable resource for improving the robustness and generalisability of automatic depression diagnosis systems.