ABSTRACT
Rationale: COVID-19 has triggered significant research activities worldwide, leading to an immense number of scientific publications in the MEDLINE. Given the high publication volume, proper combinations of structured subject headings (MeSH) and keywords are required to narrow down the search within the COVID-19 literature to the most relevant subtopic. Manual keyword selection is time-consuming and may not always be feasible. The recent text mining algorithms permit automated extraction of keywords from publications identified during pilot reference searches. It is not clear, though, whether automated keyword extraction would be useful if carried out on the pilot reference sample without prior enrichment for the COVID-19 subtopic of interest. This was addressed in the present study. Methods: A non-comprehensive MEDLINE search on the subtopic “Digital telemedicine in COVID-19” was conducted to obtain a pilot reference sample. Without manual enrichment for pertinent publications, keywords from this reference sample were extracted using two R packages, “revtools” (utilizes topic models and weighed keyword ranking) and “litsearchr” (utilizes keyword co-occurrence networks). In parallel, a manual systematic MEDLINE search strategy (MeSH concepts and headings, and manually-selected keywords, the total of 75 terms) was designed on the above topic as per PICO criteria and expert discussions. The automatically extracted keywords were then compared with the terms in manual MEDLINE search strategy. Results: The “revtools” package extracted 150 keywords from the “non-enriched” pilot reference sample. Of those, 12 (8%) keywords (either individual words or phrases) overlapped with the terms already present in the manual MEDLINE search strategy. This extraction also yielded 3 unique keywords useful to augment the manual search strategy. The “litsearchr” package extracted 203 keywords. Of those, 4 (1.97%) overlapped with the terms in the manual MEDLINE search strategy. In addition, 4 unique phrases extracted by this package were found useful for the manual search strategy. The automatically extracted and useful keywords (respectively, 3 and 4) were unique for each package. Conclusions: Automated keyword extraction, despite parallel utilization of different algorithms, cannot yet fully replace an expert-built manual MEDLINE search strategy on COVID-19. Yet this extraction, even if conducted on a non-comprehensive and non-enriched reference sample, can augment the manual search. Automated extraction from the reference sample enriched for the COVID-19 subtopic of interest may further increase the yield of useful keywords.