JMIR Publications

ABSTRACT

Background:

Pediatric influenza is a major public health concern. While Chinese patent medicines play a significant role in its treatment, information regarding drug recommendations in existing clinical guidelines is often fragmented, and the logic of syndrome differentiation-based medication is complex, limiting the efficiency of clinical decision-making.

Objective:

The objective of this study is to construct a structured Knowledge Graph for the treatment of pediatric influenza with Chinese patent medicines by integrating multiple authoritative guidelines and consensuses, and to develop an intelligent Question-Answering System based on this graph to provide precise clinical decision support.

Methods:

Using three guidelines and consensuses—including the Clinical Practice Guidelines for the Treatment of Pediatric Influenza with Chinese Patent Medicines (2024)—as core data sources, we manually and systematically extracted and standardized 11 types of entities, including influenza diagnosis, symptoms, Traditional Chinese Medicine (TCM) syndromes, therapeutic drugs, usage methods, and evidence levels. A domain ontology was constructed to define multidimensional semantic relationships between entities, and the Neo4j graph database was utilized for knowledge storage and visualization.

Results:

The constructed Knowledge Graph consists of 433 nodes and 604 relationships, integrating 22 recommended Chinese patent medicines, 21 symptoms, and 6 types of TCM syndromes. The graph visualizes the dynamic decision-making path of "symptom-syndrome-drug" and embeds GRADE evidence levels and recommendation strengths. It supports intelligent symptom-based queries and provides comprehensive decision support, including drug recommendations, specific usage and dosage, combination medication suggestions, and risk warnings for contraindications. A quantitative evaluation of the Intelligent Question-Answering System, based on 80 clinically representative queries assessed by three independent TCM experts, yielded an overall Response Accuracy of 88.8%, a Precision of 90.2%, a Recall of 87.5%, and an F1-score of 88.8%, with a Hallucination Rate of 3.8%, demonstrating the system's clinical reliability and effectiveness.

Conclusions:

This study successfully constructed a structured Knowledge Graph for the treatment of pediatric influenza with Chinese patent medicines, effectively addressing the fragmentation of knowledge in traditional guidelines. This graph assists physicians, particularly those in primary care and Western medicine practitioners, in rapidly understanding the logic of syndrome differentiation and making standardized medication decisions, thereby reducing the barrier to diagnosis and treatment of pediatric influenza. This study provides a feasible paradigm for the digital transformation of TCM guideline knowledge and the development of clinical intelligent auxiliary tools.