Evidence-Based Recommender System for a COVID-19 Publication Analytics Service
Evidence-Based Recommender System for a COVID-19 Publication Analytics Service
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DOI：
10.1109/access.2021.3083583
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Roland Oruche;Vidya Gundlapalli;Aditya P. Biswal;P. Calyam;Mauro Lemus Alarcon;Yuanxun Zhang;Naga Ramya
中科院分区：
文献类型：
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作者：
Roland Oruche;Vidya Gundlapalli;Aditya P. Biswal;P. Calyam;Mauro Lemus Alarcon;Yuanxun Zhang;Naga Ramya
COVID-19出版物的快速增长促使临床研究人员和医疗保健专业人员寻求缩小可靠信息的知识差距，以获得有效的大流行病解决方案。然而，基于证据金字塔水平检索高质量出版物的手动任务是研究人员工作流程中的一个主要瓶颈。在本文中，我们提出了一个“基于证据”的推荐系统，即KnowCOVID-19，它利用边缘计算服务来集成推荐模块，以使用最终用户瘦客户端进行数据分析。边缘计算服务具有基于聊天机器人的Web界面，使用两个推荐系统模块处理给定的COVID-19发布数据集：（i）基于证据的过滤，其观察跨文献的领域特定主题并根据临床类别对过滤的信息进行分类，以及（ii）允许具有相似目标的不同专家经由“社会平面”协作的社会过滤共同寻找关键临床问题的答案，以抗击这一流行病。我们将我们基于证据的过滤中使用的特定领域主题模型（DSTM）与考虑CORD-19数据集（COVID-19出版档案）的最先进模型进行了比较，并显示出改进的泛化效率以及知识模式查询效率。此外，我们还对人工文献综述流程和KnowCOVID-19增强流程进行了比较研究，并评估了我们的信息检索技术相对于COVID-19临床专家提供的重要查询的优势。
The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions. The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers’ workflows. In this paper, we propose an “evidence-based” recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients. The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a “social plane” to jointly find answers to critical clinical questions to fight the pandemic. We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness. In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.