Using a fine-tuned large language model for symptom-based depression evaluation.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41057559.
- Also identified by DOI 10.1038/s41746-025-01982-8 and PMC identifier 12504582.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.