Three-layered semantic framework for public health intelligence.

IF 2 3区 工程技术 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Sathvik Guru Rao, Pranitha Rokkam, Bide Zhang, Astghik Sargsyan, Abish Kaladharan, Priya Sethumadhavan, Marc Jacobs, Martin Hofmann-Apitius, Alpha Tom Kodamullil
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引用次数: 0

Abstract

Background: Disease surveillance systems play a crucial role in monitoring and preventing infectious diseases. However, the current landscape, primarily focused on fragmented health data, poses challenges to contextual understanding and decision-making. This paper addresses this issue by proposing a semantic framework using ontologies to provide a unified data representation for seamless integration. The paper demonstrates the effectiveness of this approach using a case study of a COVID-19 incident at a football game in Italy.

Method: In this study, we undertook a comprehensive approach to gather and analyze data for the development of ontologies within the realm of pandemic intelligence. Multiple ontologies were meticulously crafted to cater to different domains related to pandemic intelligence, such as healthcare systems, mass gatherings, travel, and diseases. The ontologies were classified into top-level, domain, and application layers. This classification facilitated the development of a three-layered architecture, promoting reusability, and consistency in knowledge representation, and serving as the backbone of our semantic framework.

Result: Through the utilization of our semantic framework, we accomplished semantic enrichment of both structured and unstructured data. The integration of data from diverse sources involved mapping to ontology concepts, leading to the creation and storage of RDF triples in the triple store. This process resulted in the construction of linked data, ultimately enhancing the discoverability and accessibility of valuable insights. Furthermore, our anomaly detection algorithm effectively leveraged knowledge graphs extracted from the triple store, employing semantic relationships to discern patterns and anomalies within the data. Notably, this capability was exemplified by the identification of correlations between a football game and a COVID-19 event occurring at the same location and time.

Conclusion: The framework showcased its capability to address intricate, multi-domain queries and support diverse levels of detail. Additionally, it demonstrated proficiency in data analysis and visualization, generating graphs that depict patterns and trends; however, challenges related to ontology maintenance, alignment, and mapping must be addressed for the approach's optimal utilization.

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Abstract Image

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公共卫生情报的三层语义框架。
背景:疾病监测系统在监测和预防传染病方面发挥着至关重要的作用。然而,目前的情况主要集中在零散的卫生数据上,这对背景理解和决策构成了挑战。本文通过提出一个使用本体的语义框架来解决这个问题,从而为无缝集成提供统一的数据表示。本文通过对意大利足球比赛中发生的COVID-19事件的案例研究,证明了这种方法的有效性。方法:在本研究中,我们采用了一种全面的方法来收集和分析数据,以便在流行病情报领域内开发本体论。精心设计了多个本体,以满足与流行病情报相关的不同领域,例如医疗保健系统、大规模聚会、旅行和疾病。本体被划分为顶级层、域层和应用层。这种分类促进了三层体系结构的开发,促进了知识表示的可重用性和一致性,并作为语义框架的支柱。结果:通过使用我们的语义框架,我们完成了结构化和非结构化数据的语义丰富。来自不同来源的数据的集成涉及到本体概念的映射,从而导致在三元组存储中创建和存储RDF三元组。这个过程导致了关联数据的构建,最终增强了有价值见解的可发现性和可访问性。此外,我们的异常检测算法有效地利用了从三重存储中提取的知识图,利用语义关系来识别数据中的模式和异常。值得注意的是,这种能力通过识别在同一地点和同一时间发生的足球比赛和COVID-19事件之间的相关性得到了体现。结论:该框架展示了其处理复杂、多领域查询和支持不同细节级别的能力。此外,它还展示了数据分析和可视化的熟练程度,生成了描绘模式和趋势的图形;然而,为了使该方法得到最佳利用,必须解决与本体维护、对齐和映射相关的挑战。
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来源期刊
Journal of Biomedical Semantics
Journal of Biomedical Semantics MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
4.20
自引率
5.30%
发文量
28
审稿时长
30 weeks
期刊介绍: Journal of Biomedical Semantics addresses issues of semantic enrichment and semantic processing in the biomedical domain. The scope of the journal covers two main areas: Infrastructure for biomedical semantics: focusing on semantic resources and repositories, meta-data management and resource description, knowledge representation and semantic frameworks, the Biomedical Semantic Web, and semantic interoperability. Semantic mining, annotation, and analysis: focusing on approaches and applications of semantic resources; and tools for investigation, reasoning, prediction, and discoveries in biomedicine.
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