Text mining approaches for dealing with the rapidly expanding literature on COVID-19.
Brief Bioinform
; 22(2): 781-799, 2021 03 22.
Article
in English
| MEDLINE | ID: covidwho-1352111
ABSTRACT
More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system's performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Data Mining
/
COVID-19
Type of study:
Observational study
/
Prognostic study
/
Qualitative research
/
Reviews
Limits:
Humans
Language:
English
Journal:
Brief Bioinform
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bib
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