###### Abstract  
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. depression; knowledge graph; zero-shot information extraction; large language models; DSM-5; mental health; entity linking +
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time-consuming and inefficient processes [1]. To overcome the limitations of previous methods, large language models (LLMs), which have shown excellent performance, have been actively utilized in many areas, such as information extraction [2] and prompt tuning [3]. In particular, recent studies have focused on zero-shot information extraction (ZeroIE) [4] and relation triple extraction (ZeroRTE) [5; 6], which enable pretrained models to extract information from new data. ZeroIE and ZeroRTE play pivotal roles in depression research owing to several key advantages. First, they reduce the data-labeling costs. Depression-related data are vast and manual labeling is expensive and inefficient. ZeroIE leverages pretrained models to extract valuable information from unlabeled data, thereby saving time and resources [7]. Second, ZeroIE and ZeroRTE are versatile and scalable. Depression is a complex illness characterized by a wide range of symptoms and evolving diagnostic criteria. ZeroIE models can extract information from diverse domains and contexts, enabling their application to new symptoms or diagnostic criteria without requiring model retraining. Third, ZeroIE and ZeroRTE facilitate rapid information extraction. Timely access to the latest information is critical for depression research and treatment. ZeroIE models can process and extract information in real time, thereby empowering healthcare professionals and researchers to respond promptly. Finally, ZeroIE and ZeroRTE support personalized treatment. Personalized approaches are essential for effective depression management. ZeroIE enables the rapid extraction and analysis of patient-specific information, which can facilitate the development of personalized treatment plans that optimize outcomes [8]. This study proposes a novel method for enhancing information extraction from mental disorder data by integrating medical knowledge graphs and LLMs. Our approach leverages the structured organization of knowledge graphs tailored to the rich mental health domain. We combine these knowledge graphs with the robust predictive and zero-shot learning capabilities of LLMs to extract and organize depression-related information efficiently.