Despite the excellent generalization capabilities of large-scale language models (LLMs), their severe limitations, such as illusions, lack of domain-specific knowledge, and ambiguity in the reasoning process, challenge their direct application to clinical decision support systems (CDSSs). To address these challenges, this study proposes a synergistic joint model that integrates knowledge graphs (KGs) and LLMs to enhance domain-specific knowledge and improve explainability in CDSSs. The proposed model leverages KGs to provide structured, domain-specific insights while utilizing LLMs’ generative capabilities to dynamically extract, refine, and expand medical knowledge. This bi-directional interaction ensures that CDSS recommendations remain both clinically accurate and contextually comprehensive. Performance evaluation of the joint model for mental health etiology, stress detection, and emotion recognition tasks of the CDSS showed up to a 12.0% increase in accuracy and an 8.6% increase in F1 score when compared to the standalone LLM model, with additional significant improvements when using the model with medical domain knowledge. Thus, the reliable and up-to-date domain knowledge obtained through the joint model not only improves the task performance of the CDSS, but also provides direct evidence of how such decisions were made. These findings validate the broad applicability and effectiveness of our KG–LLM joint model, highlighting its potential in real-world clinical decision support scenarios.

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health, combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management.

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management.