Leveraging Prompt Engineering and Large Language Models for Automating MADRS Score Computation for Depression Severity Assessment

This study ventures into the field of psychiatry by investigating the interactive dynamics between psychiatrists and their patients. The primary goal is to create an automated scoring mechanism using prompt engineering techniques applied to Large Language Models (LLMs) to assess the severity of depressive symptoms from these dialogues. In particular, the process of generating a depression severity score against MADRS, a rating scale widely used in psychiatry, is automated. This work aims to highlight the potential of using these techniques to improve traditional diagnostic approaches in psychiatry. The results that have emerged, while not optimal, are promising, including for the purpose of developing a full-fledged system in the future to enable the introduction of more targeted and timely interventions, thereby improving patient outcomes and improving the overall level of mental health.