## 3 Proposed Method
### Schema Definition and Annotation Guidelines
* Methodological limitations and future directions This study used a limited set of datasets, which potentially affected the generalizability of the findings. Future studies should address this issue by exploring a broader range of medical datasets and incorporating a wider variety of entity types. This will help to validate and extend the applicability of the proposed method. * Potential applications in healthcare The constructed medical knowledge graph serves as a critical tool for the systematic analysis of complex medical data and disease states. It has the potential to be integrated into real-time patient management systems to improve both diagnosis and ongoing patient care. Furthermore, the model of synergized LLMs and knowledge graphs suggests the potential benefits of integrating LLMs with knowledge graphs [24]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Existing Large Language Model**} & \multicolumn{2}{c}{**Proposed Method**} \\ \cline{2-6}  & **LLaMA2-7B** & **Flan-T5-XXL** & **LLaMA2-13B** & \
\begin{tabular}{c} **Without Entity** \\ **Linking** \\ \end{tabular} &
\begin{tabular}{c} **With Entity** \\ **Linking** \\ \end{tabular} \\ \hline DocRED & 1.2 & 4.4 & 4.0 & 7.8 & **9.8** \\ RE-DocRED & 1.9 & 4.3 & 3.5 & 7.5 & **9.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of document-level zero-shot relationship extraction methods. This approach can leverage the NLP capabilities of LLMs to interpret complex medical data and provide more accurate disease diagnosis and treatment predictions. This holds promise for more precise analysis of the diverse manifestations of depression and the development of effective personalized treatment plans. A deeper understanding of depression and other complex health conditions can be gained by advancing the methodologies outlined in this study. This will provide richer resources for healthcare professionals and researchers, ultimately improving diagnostic and treatment strategies. The synergy between LLMs and knowledge graphs not only fosters richer data interaction, but also lays the groundwork for transformative changes in medical research and practice, paving the way for innovative healthcare solutions and improved patient outcomes. ## 6 Conclusions

This study has proposed a novel approach that integrates medical knowledge graphs with LLMs to enhance information extraction for mental health disorders. Our methodology effectively addresses complex mental health conditions, such as depression, by leveraging the structural advantages of knowledge graphs and the robust predictive capabilities of LLMs. This approach significantly improves the accuracy and consistency of information extraction, particularly using entity linking and zero-shot information extraction techniques. We acknowledge the limitations of this study, particularly the limited dataset. Future research plans include validating the versatility of our methodology across diverse medical datasets and expanding the types of entities involved. These efforts are crucial to strengthen the validity of our approach further and explore its practical applicability in the healthcare sector. In conclusion, this study demonstrates significant advancements in the field of mental health through the use of medical knowledge graphs and LLMs for information extraction. It provides a powerful tool that can contribute to the diagnosis and treatment of various diseases, marking a notable step forward in integrating advanced AI technologies into healthcare. Conceptualization, C.P., H.L. and O.-r.J.; methodology, C.P. ; software, C.P. ; validation, C.P. ; formal analysis, C.P., H.L. and O.-r.J.; investigation, C.P. ; resources, C.P. ; data curation, C.P. ; writing--original draft preparation, C.P. ; writing--review and editing, C.P., H.L. and O.-r.J.; visualization, C.P. ; supervision, O.-r.J.; project administration, C.P., H.L. and O.-r.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding.