Follow‑up care after COVID-19 and its related concerns

Radvan, Martin; Bartečků, Elis; Sýkorová, Ubica; Pařízková, Radana; Richter, Svatopluk; Kameník, Martin; Koc, Lumír; Kala, Petr

doi:10.36290/vnl.2021.004

Cited by 1 publication

(1 citation statement)

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“…Simultaneously, there is an increase in demand for accurate and reliable COVID-19 information from both the medical research community and the public. Concerns about post-COVID-19 health care needs are also emerging [ 13 ] and the evidence is rapidly evolving. According to the Ontario Science Advisory Table [ 14 ], the number of people with or at risk of developing these post-COVID-19 conditions is a key concern.…”

Section: Introductionmentioning

confidence: 99%

CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice

2022

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Background Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time. Methods This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work: a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics. Results Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%. Conclusion CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions.

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Section: Introductionmentioning

confidence: 99%