• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>JMIR Medical Informatics >Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study
【24h】

Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study

机译：基于中国电子医疗记录的消化系统肿瘤知识图构建：发展与可用性研究

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background With the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem. Objective This study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics. Methods This paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer. Results Experts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of “rationality of schema structure,” “scalability,” and “readability of results,” the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its “practicability” score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor. Conclusions We constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG’s potential.
机译：背景技术随着消化系统肿瘤疾病的增加和死亡率，在中国电子医疗记录（CEMRS）中使用临床经验数据来确定诊断和治疗之间的潜在有效关系已成为优先事项。作为人工智能的重要组成部分，知识图形是信息处理和知识组织的强大工具，提供解决解决此问题的理想手段。目的本研究旨在构建语义驱动的消化系统肿瘤知识图（DSTKG），以代表CEMRS的知识，具有细粒度和语义。方法本文侧重于知识图形模式和语义关系，这是构建中国肿瘤知识图的主要挑战。 DSTKG是通过多步骤制定的。作为初步步骤，提出了基于CEMRS的完整DSTKG施工框架。然后，这项研究建立了一个包含7类和16种语义关系的知识图形模式，并通过知识提取，命名实体链接以及绘制知识图形来完成DSTKG。最后，从3个方面评估DSTKG的质量：数据层，架构层和应用层。结果专家同意，DSTKG整体良好（平均得分4.20）。特别是对于“模式结构的合理性”的方面，“可扩展性”和“结果的可读性”，DSTKG表现良好，分别为4.72,4.67和4.69，其分别远高于平均值。然而，DSTKG中的少量数据对其“实用性”得分负面影响。与其他中国肿瘤知识图相比，DSTKG可以代表更粒度的实体，属性和语义关系。此外，DSTKG是灵活的，允许个性化的定制，以满足设计师对消化系统肿瘤的特定兴趣的关注。结论我们建造了一种粒状语义DSTKG。它可以为构建肿瘤知识图提供指导，并为基于CEMRS提供知识图形智能应用的初步步骤。需要对断言分类的额外数据来源和更强大的研究来深入了解DSTKG的潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号