Research on the Proximity Relationships of Psychosomatic Disease Knowledge Graph Modules Extracted by Large Language Models

TL;DR

This study constructs a knowledge graph of psychosomatic diseases using large language models, revealing how network proximity relates to clinical similarities and aiding diagnosis and treatment strategies.

Contribution

It introduces a novel approach combining LLMs and network analysis to explore psychosomatic disease relationships and enhances understanding of disease-symptom-treatment connections.

Findings

Closer network distances among diseases predict similar clinical features.

Symptoms that are closer tend to co-occur more frequently.

Symptom-disease pairs in primary diagnostic relationships are more strongly associated.

Abstract

As social changes accelerate, the incidence of psychosomatic disorders has significantly increased, becoming a major challenge in global health issues. This necessitates an innovative knowledge system and analytical methods to aid in diagnosis and treatment. Here, we establish the ontology model and entity types, using the BERT model and LoRA-tuned LLM for named entity recognition, constructing the knowledge graph with 9668 triples. Next, by analyzing the network distances between disease, symptom, and drug modules, it was found that closer network distances among diseases can predict greater similarities in their clinical manifestations, treatment approaches, and psychological mechanisms, and closer distances between symptoms indicate that they are more likely to co-occur. Lastly, by comparing the proximity d and proximity z score, it was shown that symptom-disease pairs in primary diagnostic relationships have a stronger association and are of higher referential value than those in diagnostic relationships. The research results revealed the potential connections between diseases, co-occurring symptoms, and similarities in treatment strategies, providing new perspectives for the diagnosis and treatment of psychosomatic disorders and valuable information for future mental health research and practice.