Version 1
: Received: 4 June 2024 / Approved: 4 June 2024 / Online: 4 June 2024 (09:26:37 CEST)
How to cite:
Park, C.; Lee, H.; Jeong, O. Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction. Preprints2024, 2024060162. https://doi.org/10.20944/preprints202406.0162.v1
Park, C.; Lee, H.; Jeong, O. Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction. Preprints 2024, 2024060162. https://doi.org/10.20944/preprints202406.0162.v1
Park, C.; Lee, H.; Jeong, O. Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction. Preprints2024, 2024060162. https://doi.org/10.20944/preprints202406.0162.v1
APA Style
Park, C., Lee, H., & Jeong, O. (2024). Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction. Preprints. https://doi.org/10.20944/preprints202406.0162.v1
Chicago/Turabian Style
Park, C., Hayoung Lee and Okran Jeong. 2024 "Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction" Preprints. https://doi.org/10.20944/preprints202406.0162.v1
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.
Keywords
Depression; Knowledge Graph; Zero-shot Information Extraction; Large Language Models; DSM-5; Mental Health; Entity Linking
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
