Abstract
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.
Highlights
With the impact of the COVID-19 causing a major societal crisis, scientific research has been crucial in terms of clinical researchers and healthcare professionals accessing publicly available medical journal databases (e.g., PubMed [1], LitCovid [2]) to perform knowledge discovery
We propose a novel ‘‘evidence-based’’ recommender system namely KnowCOVID-19 for clinical researchers, healthcare professionals to automatically filter high-quality publications according to the evidence-based information filtering standard
EVALUATION CASE STUDY We evaluated the effectiveness of KnowCOVID-19 by utilizing the CORD-19 dataset to compare with a manual analysis of sorting through publications without any resources provided by a team of clinical researchers
Summary
With the impact of the COVID-19 causing a major societal crisis, scientific research has been crucial in terms of clinical researchers and healthcare professionals accessing publicly available medical journal databases (e.g., PubMed [1], LitCovid [2]) to perform knowledge discovery. The ability to combat pandemic-related problems through accessible literature archives can be accomplished using thin-clients such as web browsers. Such an approach allows real-time access to data resources and analysis tools that are hosted on a remote server versus having users download data and install tools to a localized hard drive to perform analysis. It allows clinical researchers and healthcare professionals. Handling continuously-growing publication databases at the researchers’ disposal today is performed manually, which makes it onerous and time consuming to filter out high-quality data in literature archives
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