Evidence-Based Recommender System for a COVID-19 Publication Analytics Service
| Autor: | Prasad Calyam, Aditya P. Biswal, Mauro Lemus Alarcon, Vidya Gundlapalli, Roland Oruche, Abhiram Malladi, Naga Ramya Bhamidipati, Hariharan Regunath, Yuanxun Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Topic model
Service (systems architecture) General Computer Science Computer science 02 engineering and technology Recommender system computer.software_genre literature review automation Chatbot Data modeling 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering General Materials Science Edge computing 030304 developmental biology recommender system 0303 health sciences Information retrieval business.industry social networking General Engineering 020206 networking & telecommunications COVID-19 publication analytics TK1-9971 Workflow machine learning Analytics Electrical engineering. Electronics. Nuclear engineering business computer |
| Zdroj: | IEEE Access, Vol 9, Pp 79400-79415 (2021) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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