Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.
Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.
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DOI：
10.2196/26892
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发表时间：
2021-06-15
影响因子：
7.4
通讯作者：
Jiang T
中科院分区：
文献类型：
--
作者：
Deng L;Chen L;Yang T;Liu M;Li S;Jiang T
表型表征疾病的临床表现，并为诊断提供重要信息。因此，疾病表型知识图谱的构建对人工智能在医学上的发展具有重要意义。然而，维基数据和DBpedia等现有知识库中的表型知识图是粗粒度的知识图，因为它们只考虑表型的核心概念，而忽略了与这些表型相关的细节(属性)。为了描述临床指南中疾病表型的细节，我们提出了一个细粒度的语义信息模型PhenoSSU(语义表型结构单元)。PhenoSSU本质上是一个“实体-属性-值”模型，它的目标是通过一系列属性和值来捕获表现型描述背后的全部语义信息。选取来自维基百科的193篇传染病临床指南作为研究语料库，基于表型概念和属性值的共现，将SNOMED-CT中的12个属性引入PhenoSSU模型。通过分析PhenoSSU实例是否能够捕获对应表型描述的完整语义来评估PhenoSSU模型的表达能力。为了自动构建细粒度的表型知识图，提出了一种先用MetaMap工具识别表型概念，然后用机器学习分类器预测表型属性值的混合策略。利用Brat标注工具手工构建了193种传染病的细粒度表型知识图谱。在这些知识图中总共标注了4020个PhenoSSU实例，其中3757个实例(89.5%)能够捕获临床指南中列出的相应表型描述的完整语义。相比之下，其他信息模型，如临床要素模型和HL7快速卫生保健互操作性资源模型，分别只能捕获48.4%(2034/4020)和21.8%(914/4020)临床指南中列出的表型描述的完整语义。该策略对表型概念识别子任务的F1-Score为0.732，对属性值预测子任务的平均加权准确率为0.776。PhenoSSU是一种用于精确表示临床指南表型知识的有效信息模型，机器学习可以用来提高基于PhenoSSU的知识图的构建效率。我们的工作可能会将医学知识工程的重点从粗粒度水平转移到更细粒度水平。
Phenotypes characterize the clinical manifestations of diseases and provide important information for diagnosis. Therefore, the construction of phenotype knowledge graphs for diseases is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs because they only consider the core concepts of phenotypes while neglecting the details (attributes) associated with these phenotypes. To characterize the details of disease phenotypes for clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (semantic structured unit of phenotypes). PhenoSSU is an “entity-attribute-value” model by its very nature, and it aims to capture the full semantic information underlying phenotype descriptions with a series of attributes and values. A total of 193 clinical guidelines for infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on the co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether PhenoSSU instances could capture the full semantics underlying the descriptions of the corresponding phenotypes. To automatically construct fine-grained phenotype knowledge graphs, a hybrid strategy that first recognized phenotype concepts with the MetaMap tool and then predicted the attribute values of phenotypes with machine learning classifiers was developed. Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. A total of 4020 PhenoSSU instances were annotated in these knowledge graphs, and 3757 of them (89.5%) were found to be able to capture the full semantics underlying the descriptions of the corresponding phenotypes listed in clinical guidelines. By comparison, other information models, such as the clinical element model and the HL7 fast health care interoperability resource model, could only capture the full semantics underlying 48.4% (2034/4020) and 21.8% (914/4020) of the descriptions of phenotypes listed in clinical guidelines, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction. PhenoSSU is an effective information model for the precise representation of phenotype knowledge for clinical guidelines, and machine learning can be used to improve the efficiency of constructing PhenoSSU-based knowledge graphs. Our work will potentially shift the focus of medical knowledge engineering from a coarse-grained level to a more fine-grained level.
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DOI：
10.1136/amiajnl-2013-001945
发表时间：
2013-12-01
影响因子：
6.4
作者：
Chen, Yukun;Carroll, Robert J.;Xu, Hua
通讯作者：
Xu, Hua
影响因子：
4.5
作者：
Harkema H;Dowling JN;Thornblade T;Chapman WW
通讯作者：
Chapman WW
影响因子：
5
作者：
Chawla, NV;Bowyer, KW;Kegelmeyer, WP
通讯作者：
Kegelmeyer, WP
DOI：
10.1093/jamia/ocw177
发表时间：
2017-07-01
影响因子：
6.4
作者：
Demner-Fushman, Dina;Rogers, Willie J.;Aronson, Alan R.
通讯作者：
Aronson, Alan R.
DOI：
10.1093/jamia/ocx090
发表时间：
2018-01-01
影响因子：
6.4
作者：
Luo, Yuan;Cheng, Yu;Starren, Justin
通讯作者：
Starren, Justin