构建肝癌知识图谱，用新模型提升医学实体识别准确率。
Liver Cancer Knowledge Graph Construction based on dynamic entity replacement and masking strategies RoBERTa-BiLSTM-CRF model
- 设计动态实体替换与掩码策略，优化实体识别
- 识别准确率达93.23%，召回94.69%，F1为93.96%
- 适合医疗AI研究者与智能诊疗系统开发者
肝癌是我国第五大常见恶性肿瘤，也是第二大致死原因。早期诊断至关重要，但临床诊断需综合分析患者体征、症状、病史及各类检查结果，数据形式多样且处理复杂，给医生带来巨大负担。为此，构建肝癌知识图谱辅助诊疗系统符合国家智慧医疗发展方向。本文针对肝癌诊断知识图谱构建中的核心挑战——公共数据与真实电子病历之间的差异，提出六步构建流程：概念层设计、数据预处理、实体识别、实体归一化、知识融合与图可视化。创新性地提出动态实体替换与掩码策略（DERM），用于命名实体识别。最终建成包含7类实体（如疾病、症状、体质等）的肝癌知识图谱，共含1495个实体。模型在实体识别上达到准确率93.23%、召回率94.69%、F1分数93.96%的性能表现。
原文摘要 · Abstract (English)
Background: Liver cancer ranks as the fifth most common malignant tumor and the second most fatal in our country. Early diagnosis is crucial, necessitating that physicians identify liver cancer in patients at the earliest possible stage. However, the diagnostic process is complex and demanding. Physicians must analyze a broad spectrum of patient data, encompassing physical condition, symptoms, medical history, and results from various examinations and tests, recorded in both structured and unstructured medical formats. This results in a significant workload for healthcare professionals. In response, integrating knowledge graph technology to develop a liver cancer knowledge graph-assisted diagnosis and treatment system aligns with national efforts toward smart healthcare. Such a system promises to mitigate the challenges faced by physicians in diagnosing and treating liver cancer. Methods: This paper addresses the major challenges in building a knowledge graph for hepatocellular carcinoma diagnosis, such as the discrepancy between public data sources and real electronic medical records, the effective integration of which remains a key issue. The knowledge graph construction process consists of six steps: conceptual layer design, data preprocessing, entity identification, entity normalization, knowledge fusion, and graph visualization. A novel Dynamic Entity Replacement and Masking Strategy (DERM) for named entity recognition is proposed. Results: A knowledge graph for liver cancer was established, including 7 entity types such as disease, symptom, and constitution, containing 1495 entities. The recognition accuracy of the model was 93.23%, the recall was 94.69%, and the F1 score was 93.96%.
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