Using a fine-tuned large language model for symptom-based depression evaluation
Year: 2025
Type: article
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.
DOI: 10.5167/uzh-280629
Authors: Weber, Samantha, Deperrois, Nicolas, Heun, Robert, Frühschütz, Laura, Monn, Anna, Homan, Stephanie, Häfliger, Andrea, Seifritz, Erich, Kowatsch, Tobias, Jäger, Lena, Schultebraucks, Katharina, Gershov, Sapir, Mocellin, Jacopo, Kleim, Birgit, Olbrich, Sebastian
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7106327185
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