Machine Learning Maps Research Needs in COVID-19 Literature
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Abstract
Summary
Manually assessing the scope of the thousands of publications on the COVID-19 (coronavirus disease 2019) pandemic is an overwhelming task. Shortcuts through metadata analysis (e.g., keywords) assume that studies are properly tagged. However, machine learning approaches can rapidly survey the actual text of coronavirus abstracts to identify research overlap between COVID-19 and other coronavirus diseases, research hotspots, and areas warranting exploration. We propose a fast, scalable, and reusable framework to parse novel disease literature. When applied to the COVID-19 Open Research Dataset (CORD-19), dimensionality reduction suggested that COVID-19 studies to date are primarily clinical-, modeling- or field-based, in contrast to the vast quantity of laboratory-driven research for other (non-COVID-19) coronavirus diseases. Topic modeling also indicated that COVID-19 publications have thus far focused primarily on public health, outbreak reporting, clinical care, and testing for coronaviruses, as opposed to the more limited number focused on basic microbiology, including pathogenesis and transmission.
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SciScore for 10.1101/2020.06.11.145425: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Peer-reviewed studies from PubMed/PubMed Central, as well as pre-prints from bioRxiv and medRxiv, are retrieved using specific coronavirus-related keywords (“COVID-19” OR “Coronavirus” OR “Corona virus” OR “2019-nCoV”OR “SARS-CoV” OR “MERS-CoV” OR “Severe Acute Respiratory Syndrome” OR “Middle East Respiratory Syndrome”). bioRxivsuggested: (bioRxiv, RRID:SCR_003933)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following …SciScore for 10.1101/2020.06.11.145425: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Peer-reviewed studies from PubMed/PubMed Central, as well as pre-prints from bioRxiv and medRxiv, are retrieved using specific coronavirus-related keywords (“COVID-19” OR “Coronavirus” OR “Corona virus” OR “2019-nCoV”OR “SARS-CoV” OR “MERS-CoV” OR “Severe Acute Respiratory Syndrome” OR “Middle East Respiratory Syndrome”). bioRxivsuggested: (bioRxiv, RRID:SCR_003933)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:These conclusions must be caveated by several limitations that must be acknowledged. First, while CORD-19 includes a vast quantity of coronavirus-related publications, it potentially omits relevant literature from other databases, such as the Social Science Research Network (SSRN) or arXiv (a preprint server for studies in mathematics, computer science, and quantitative biology, among other topics). This may have constrained the representativeness of our analysis on COVID-19 literature, thus affecting the external validity of our findings. Second, analyzing abstracts inherently excludes ongoing research efforts because not all relevant studies are publicly available or have released preprints. Third, the number of publications does not directly represent progress in research areas. Fourth, the high-level trends we observed through our unsupervised ML approaches may not completely align with how researchers identify and process specific research topics. The counts of words in DTMs informing the ML algorithms may not directly capture the ideas researchers are trying to convey and may therefore gloss over nuances in the literature. Yet these four limitations are somewhat mitigated by both the nature of the data sources and the needs of the research community. For the first, the excluded sources (SSRN and arXiv) heavily focus on research within the CMF arena, indicating that if anything, our conclusions on the rapid pace of CMF COVID-19 research (versus lab-based research) are co...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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