This research proposes a novel method using Large Language Models to detect early signs of depression in Spanish texts with human-interpretable results.

• Employs specialist reasoning criteria to analyze user text data
• Focuses on in-context learning approaches for Spanish language processing
• Creates interpretable assessment outputs that clinicians can understand
• Demonstrates the application of LLMs in mental health monitoring

This work addresses a critical need in mental healthcare by leveraging AI to identify at-risk individuals earlier, potentially enabling timely intervention while maintaining transparency in the assessment process.

Hacia la interpretabilidad de la detección anticipada de riesgos de depresión utilizando grandes modelos de lenguaje