A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph

Summary

A gynecology-specific acupuncture knowledge graph and decision-support system for diminished ovarian reserve (DOR) was developed using literature, classical texts, and clinical data. Clinical evaluation demonstrated 86.7% diagnostic agreement and high prescription scores, with statistically significant but limited inter-expert agreement, supporting its role as an auxiliary clinical decision-support tool.

Abstract

Diminished ovarian reserve (DOR) is increasing in incidence and affecting younger women. Acupuncture has been used clinically for DOR; however, practice remains subjective, experience-dependent, and lacks standardized guideline implementation. Building on our previous general intelligent acupuncture system, this study targets gynecological diseases and uses DOR as the clinical entry point to develop a specialized knowledge graph and decision-support system. This study included 82 classical acupuncture texts, Chinese and English literature, and clinical data. Knowledge was extracted using a hybrid approach combining large language models and manual annotation to construct a full acupuncture knowledge graph and a DOR-specific gynecology subgraph. The system employs BiLSTM-CRF-based entity recognition and dual-mode retrieval to automate syndrome differentiation and acupuncture prescription generation. The full graph contains 16,558 entities and 80,084 relations, while the gynecology subgraph contains 8,677 entities and 27,092 relations. In a clinical evaluation of 90 patients with DOR, the system achieved a diagnostic agreement rate of 86.7% compared with expert assessments. The mean prescription appropriateness score was 4.65. Inter-rater agreement among experts, measured using Kendall’s coefficient of concordance, was W ≈ 0.13 (P < 0.05), indicating statistically significant but limited agreement. These findings suggest that the system can provide structured support for acupuncture-based syndrome differentiation and prescription generation in DOR, with potential as an auxiliary clinical decision-support tool.

Introduction

Diminished ovarian reserve (DOR) is characterized by a decline in the quantity and/or quality of oocytes, leading to reduced fertility, menstrual irregularities, and hormonal imbalance¹. The incidence of DOR has increased in recent years and is progressively affecting younger populations. Epidemiological data from the United States indicate an increase from 19% in 2004 to 26% in 2011², while a report from the Chinese Medical Association documented a prevalence of 13.58% among infertile patients undergoing assisted reproductive technology^2,3.

Protocol

This study was conducted in accordance with institutional ethical standards and was approved by the Ethics Committee of the General Hospital of Eastern Theater Command (Approval No. DZQH-KYLLFS-25-38). Written informed consent was obtained from all participants prior to inclusion. All patient data were anonymized and de-identified to ensure privacy protection. The research tools for this protocol have been listed in the Table of Materials.

1. Knowledge graph construction

The knowledge graph was constructed using a multi-source data integration strategy. Data collection and annotati....

Results

The constructed knowledge graph integrates heterogeneous data from classical traditional Chinese medicine texts, modern clinical literature, and real-world case records, following established principles of knowledge graph construction and evaluation. A total of 374 Chinese-language sources, 267 English-language articles, and 82 classical TCM texts were incorporated, ensuring both historical depth and contemporary clinical relevance (Figure 5).

Discussion

The present study describes a knowledge graph-based intelligent decision-support system tailored for acupuncture treatment of diminished ovarian reserve, representing a transition from general-purpose traditional Chinese medicine knowledge modeling to a disease-specific clinical application.

Compared with conventional acupuncture practice, which relies heavily on practitioner experience, the proposed system introduces a structured and reproducible diagnostic framework. By integrating multi-sou.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BiLSTM-CRF	Open Source (https://github.com/jmhIcoding/bilstm-crf)	N/A	Deep learning model architecture employed for named entity recognition in clinical text extraction.
CNKI Database	China National Knowledge Infrastructure	N/A	Literature repository utilized for the retrieval of relevant Chinese-language academic publications.
JSON Viewer & Formatter	Megaish	1.6.0	Software tool used for JSON data structure viewing and validation.
KGs Annotation Platform	Nanjing KGI Data Technology Co., Ltd.	N/A	Web-based collaborative annotation platform utilized for the manual labeling of medical entity and relationship corpora to support knowledge graph construction.
Large Language Model API	Moonshot AI	N/A	Kimi LLM API employed for semantic understanding and automated knowledge extraction from biomedical literature; interface compatible with OpenAI API format.
MySQL	Oracle Corporation	8.0.42	Relational database management system used for the storage of structured clinical data.
Neo4j Browser	Neo4j,Inc.	5.26.10	Graph database interface used for the interactive visualization and exploratory querying of the gynecological knowledge graph.
Python	Python Software Foundation	3.11.4	Primary programming language utilized for automated literature crawling, LLM API invocation, multiprocessing coordination, data parsing, and knowledge graph construction.
Web of Science Core Collection	Clarivate Analytics	N/A	Literature repository utilized for the retrieval of relevant English-language academic publications.