基于超声检查报告的乳腺癌诊断知识图谱构建

Construction of Knowledge Graph for Breast Cancer Diagnosis Based on Ultrasound Examination Reports

乳腺癌电子病历承载着丰富的乳腺癌诊断和治疗信息。对这些病历文本进行知识抽取并构建相应的知识图谱,有助于面向乳腺癌诊断的智能化数据分析及辅助决策支持系统的构建。本文以乳腺癌超声检查报告为研究样本,在知识抽取、知识图谱构建和应用开展实例研究。结合乳腺癌诊疗指南和临床专家经验,构建了乳腺癌诊断知识图谱本体及概念层;基于BiLSTM-Attention-CRF模型,开展了针对超声检查报告的知识抽取工作,模型的准确率、召回率及F1值均达到了96%以上;最后,构建知识图谱并开展可视化分析、数据查询、辅助诊断等应用研究。结果表明,所构建的知识图谱在帮助提高电子病历数据质量、提升临床诊疗效果和改善医学决策性能等方面具有较大的应用价值。

Electronic medical records of breast cancer contain rich information about breast cancer diagnosis and treatment, from which features can be extracted to construct a knowledge graph. This is helpful for the construction of both intelligent user interface and computer-assisted decision support systems of breast cancer. In this paper, a case study on feature extraction, knowledge graph construction, and application is carried out using breast cancer ultrasound examination reports. First, the entities and conceptual structure of the knowledge graph for breast cancer diagnosis are defined based on the diagnosis and treatment guidelines and clinical expert experiences. Then, based on the BiLSTM-CRF model and attention mechanism, feature extraction from ultrasonic examination reports is carried out. The results show that the accuracy rate, the recall rate, and the F1 value reach more than 96%.Finally, the knowledge graph is constructed and applied to data query, visual analysis, and computer-assisted diagnosis. The results show that it has great application value in helping improve the quality of electronic medical records, the effects of clinical diagnosis and treatment, and the performance of medical decision-making.