Application of Large Language Model in Mental Health Clinical Decision Support System
DOI:
https://doi.org/10.71222/nbtsxh74Keywords:
large language model, mental health, clinical decision support, prompt engineering, interpretabilityAbstract
With the rapid advancement of artificial intelligence, large language models (LLMs) have emerged as potentially transformative auxiliary tools for clinical judgment and decision-making within the mental health domain. This paper explores systematic methodologies for integrating these models into psychiatric practice, focusing specifically on technical applications in semantic modeling, sophisticated data processing strategies, and the refinement of prompt engineering alongside output optimization. A robust architectural framework and integration scheme tailored to the specificities of mental health services are constructed, providing a structured approach to clinical deployment. Furthermore, the study establishes comprehensive evaluation model standards and rigorous risk control measures designed to enhance the functional stability and ethical safety of these systems. By addressing critical concerns such as interpretability and clinical reliability, this research aims to provide a clear technical trajectory and implementation support for achieving intelligent, evidence-based, and highly understandable auxiliary decision-making in modern mental health care environments.References
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