Using a fine-tuned large language model for symptom-based depression evaluation

2025/10/07 by Weber, Samantha, Deperrois, Nicolas, Heun, Robert +12
Psychology · #610 Medicine &amp #Digital Mental Health Interventions #Mental Health Treatment and Access #Mental Health via Writing #health

paper · doi:10.5167/uzh-280629

openalex publication_date 2025/10/07 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/28

Abstract

Recent advances in artificial intelligence, particularly large language models (LLMs), show promise for mental health applications, including the automated detection of depressive symptoms from natural language. We fine-tuned a German BERT-based LLM to predict individual Montgomery-Åsberg Depression Rating Scale (MADRS) scores using a regression approach across different symptom items (0–6 severity scale), based on structured clinical interviews with transdiagnostic patients as well as synthetically generated interviews. The fine-tuned model achieved a mean absolute error of 0.7–1.0 across items, with accuracies ranging from 79 to 88%, closely matching clinician ratings. Fine-tuning resulted in a 75% reduction in prediction errors relative to the untrained model. These findings demonstrate the potential of lightweight LLMs to accurately assess depressive symptom severity, offering a scalable tool for clinical decision-making, and monitoring treatment progress, particularly in low-resource settings.

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