ArticleFrontiers in psychiatry2024
M.I.N.I.-KID interviews with adolescents: a corpus-based language analysis of adolescents with depressive disorders and the possibilities of continuation using Chat GPT.
Article in Frontiers in psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Evaluating large language models for assessment of psychosis risk.NPJ digital medicine · 2026Article
- Assessing large language model responses to pediatric depression FAQs: a cross-sectional study on readability, accuracy, and sentiment.Frontiers in psychiatry · 2026Article
- From promise to practice: insights into ChatGPT-4o use in child and adolescent mental health from professionals.Frontiers in psychiatry · 2025Article
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9 authors.
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Abstract
Background: Up to 13% of adolescents suffer from depressive disorders. Despite the high psychological burden, adolescents rarely decide to contact child and adolescent psychiatric services. To provide a low-barrier alternative, our long-term goal is to develop a chatbot for early identification of depressive symptoms. To test feasibility, we followed a two-step procedure, a) collection and linguistic analysis of psychiatric interviews with healthy adolescents and adolescents with depressive disorders and training of classifiers for detection of disorders from their answers in interviews, and b) generation of additional adolescent utterances via Chat GPT to improve the previously created model. Methods: For step a), we collected standardized interviews with 53 adolescents, Results: In step a), the classifier based on BERT was able to discriminate answers by adolescents with and without depression with accuracies up to 97% and identified commonly used words and phrases. Evaluating the quality of utterances generated in step b), we found that prompt engineering for this task is difficult as Chat GPT performs poorly with long prompts and abstract descriptions of expectations on appropriate responses. The best approach was to cite original answers from the transcripts in order to optimally mimic the style of language used by patients and to find a practicable compromise between the length of prompts that Chat GPT can handle and the number of examples presented in order to minimize literal repetitions in Chat GPT's output. Conclusion: The results indicate that identifying linguistic patterns in adolescents' transcribed verbal responses is promising and that Chat GPT can be leveraged to generate a large dataset of interviews. The main benefit is that without any loss of validity the synthetic data are significantly easier to obtain than interview transcripts.
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