IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024
10 – 13 November 2024 Houston, TX USA
An Automated Analysis Framework for Epidemiological Survey on COVID-19
Lin, Zichao; Lin, Xialv; Yang, Xu
The article introduces an Automated Visual Epidemiological Survey Analysis (AVESA) framework designed for the epidemiological survey and analysis of COVID-19. Traditional methods of analyzing epidemiological survey reports, which are predominantly manual, encounter challenges such as significant time consumption and a heavy dependence on manual labor. The AVESA framework addresses these challenges by leveraging deep neural networks to extract information from case reports and by constructing an epidemiological knowledge graph using predefined patterns. Furthermore, AVSEA utilizes a multi-dimensional knowledge reasoning model to facilitate reasoning within the comprehensive COVID-19 epidemiological knowledge graph.
The epidemiological knowledge graph constructed within the framework includes a pattern layer for the storage structure and a data layer for the organization of specific data, forming the foundation for knowledge reasoning. This graph depicts the connections and relationships between entities, such as individuals and locations, along with their attributes, thereby simplifying the process of deriving insights from case reports. The knowledge reasoning model supports both comprehensive graph computing and sub-graph matching, facilitating the identification of close contacts, the assessment of high-risk areas, and the elucidation of potential transmission chains. The framework significantly enhances performance in both entity extraction and multi-task extraction sub-tasks, with knowledge reasoning results that are highly consistent with human analyses.
Experimental results demonstrate the framework’s efficacy and advantages in processing epidemiological data, positioning it as a valuable tool for enhancing the efficiency and precision of epidemiological surveys. Beyond merely improving upon traditional manual analysis methods, the AVESA framework offers a scalable and flexible solution for a wide range of applications in epidemiology, capable of managing large-scale data across various infectious diseases.
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