Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
Comput Methods Programs Biomed. 2018 Oct;165:117-128. doi: 10.1016/j.cmpb.2018.08.010. Epub 2018 Aug 16.
The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requires powerful novel technologies and architectures to unlock the information hidden in the unstructured data. The success of smart healthcare applications such as clinical decision support systems, disease diagnosis systems, and healthcare management systems depends on knowledge that is understandable by machines to interpret and infer new knowledge from it. In this regard, ontological data models are expected to play a vital role to organize, integrate, and make informative inferences with the knowledge implicit in that unstructured data and represent the resultant knowledge in a form that machines can understand. However, constructing such models is challenging because they demand intensive labor, domain experts, and ontology engineers. Such requirements impose a limit on the scale or scope of ontological data models. We present a framework that will allow mitigating the time-intensity to build ontologies and achieve machine interoperability.
Empowered by linked biomedical ontologies, our proposed novel Automated Ontology Generation Framework consists of five major modules: a) Text Processing using compute on demand approach. b) Medical Semantic Annotation using N-Gram, ontology linking and classification algorithms, c) Relation Extraction using graph method and Syntactic Patterns, d), Semantic Enrichment using RDF mining, e) Domain Inference Engine to build the formal ontology.
Quantitative evaluations show 84.78% recall, 53.35% precision, and 67.70% F-measure in terms of disease-drug concepts identification; 85.51% recall, 69.61% precision, and F-measure 76.74% with respect to taxonomic relation extraction; and 77.20% recall, 40.10% precision, and F-measure 52.78% with respect to biomedical non-taxonomic relation extraction.
We present an automated ontology generation framework that is empowered by Linked Biomedical Ontologies. This framework integrates various natural language processing, semantic enrichment, syntactic pattern, and graph algorithm based techniques. Moreover, it shows that using Linked Biomedical Ontologies enables a promising solution to the problem of automating the process of disease-drug ontology generation.
生物医学文献和电子健康记录 (EHR) 中可用的非结构化数据呈指数级增长，这需要强大的新技术和架构来解锁隐藏在非结构化数据中的信息。临床决策支持系统、疾病诊断系统和医疗保健管理系统等智能医疗应用的成功取决于机器可理解的知识，以便从中解释和推断新知识。在这方面，本体论数据模型有望发挥重要作用，以组织、整合和对隐含在非结构化数据中的知识进行信息丰富的推理，并以机器可以理解的形式表示结果知识。然而，构建这样的模型具有挑战性，因为它们需要大量的劳动、领域专家和本体工程师。这些要求限制了本体论数据模型的规模或范围。我们提出了一个框架，该框架将允许减轻构建本体的时间强度并实现机器互操作性。
在链接生物医学本体的支持下，我们提出的新型自动化本体生成框架由五个主要模块组成：a）使用按需计算的文本处理。b）使用 N 元组、本体链接和分类算法进行医学语义注释。c）使用图方法和语法模式进行关系提取。d）使用 RDF 挖掘进行语义丰富。e）使用领域推理引擎构建形式本体。
定量评估显示，在疾病-药物概念识别方面，84.78%的召回率、53.35%的准确率和 67.70%的 F1 分数；在分类关系提取方面，85.51%的召回率、69.61%的准确率和 76.74%的 F1 分数；在生物医学非分类关系提取方面，77.20%的召回率、40.10%的准确率和 52.78%的 F1 分数。
我们提出了一个由链接生物医学本体驱动的自动化本体生成框架。该框架集成了各种自然语言处理、语义丰富、语法模式和图算法技术。此外，它表明使用链接生物医学本体为自动化疾病-药物本体生成过程提供了有前途的解决方案。
