Artificial-intelligence tools aim to tame the coronavirus literature

Hutson, Matthew

doi:10.1038/d41586-020-01733-7

Cited by 26 publications

(24 citation statements)

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“…Over 30 major academic publishers, including Elsevier, Wiley, Springer, Taylor and Francis, and others unlocked tens of thousands articles (Change, 2020; Wellcome, 2020a). Artificial-intelligence tools to extract specific findings and bridge fields to navigate the fast-rising number of research papers were being developed as well (Hutson, 2020). Publications in machine-readable formats and open licenses were especially key for this purpose (Wellcome 2020b).…”

Section: Scientific Globalismmentioning

confidence: 99%

Scientific globalism during a global crisis: research collaboration and open access publications on COVID-19

2020

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This study sought to understand the nature of scientific globalism during a global crisis, particularly COVID-19. Findings show that scientific globalism occurs differently when comparing COVID-19 publications with non-COVID-19 publications during as well as before the pandemic. Despite the tense geopolitical climate, countries increased their proportion of international collaboration and open-access publications during the pandemic. However, not all countries engaged more globally. Countries that have been more impacted by the crisis and those with relatively lower GDPs tended to participate more in scientific globalism than their counterparts.

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Section: Scientific Globalismmentioning

confidence: 99%

Scientific globalism during a global crisis: research collaboration and open access publications on COVID-19

2020

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“…Major publishers have all expedited the peer review and publication of research on COVID-19 (Horbach, 2020), resulting in an impressive growth in the literature on this subject that has surpassed 3,500 new titles per week by mid-March 2020 (Microsoft Research, 2020). After 3 months, the growth in the research literature only sees acceleration and no sign of abating (Hutson, 2020). Facing the daunting task of tracking the voluminous new studies arriving at an unprecedented rate and motivated by the remarkable advancements in artificial intelligence (AI) in recent years, the U.S. Office of Science and Technology Policy in the White House (WH/OSTP) has challenged the research community to develop intelligent agents that can effectively sift through the literature and assist scientists and policy makers alike.…”

Section: Introductionmentioning

confidence: 99%

“…Facing the daunting task of tracking the voluminous new studies arriving at an unprecedented rate and motivated by the remarkable advancements in artificial intelligence (AI) in recent years, the U.S. Office of Science and Technology Policy in the White House (WH/OSTP) has challenged the research community to develop intelligent agents that can effectively sift through the literature and assist scientists and policy makers alike. Specifically, the WH/OSTP has led the launch of an open question-answering challenge hosted on Kaggle ( Kaggle, 2020 ) and three new tracks in the long-running Text REtrieval Conference organized by the National Institute of Standards and Technology that has given birth to pivotal theoretical and technological components behind modern Web search engines and conversational systems. The COVID-19 Research Dataset (CORD-19) has been created to support these efforts.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature

Kanakia

Wang

Dong

et al. 2020

Front. Res. Metr. Anal.

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On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned “A Century of Physics” analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.

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“…Model-building is an iterative exercise that requires lab data from testing to be evaluated by credible scientists before data scientists can use it (curate?) to train ML models, which are error prone 27 .…”

Section: C2ddmentioning

confidence: 99%

Aptamers for Detection and Diagnostics (ADD) is a proposed mobile app acquiring optical data from conjugated quantum nanodots to identify molecules indicating presence of SARS-CoV-2 virus: Why public health and healthcare need smartphone sensors as a platform for early detection and prevention

Datta

2021

Preprint

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Proposed SARS-CoV-2 surveillance tool using a mobile app for non-invasive monitoring of humans and animals. <p>Engineering a biomedical device as a low-cost, non-invasive, detection, and diagnostic platform for surveillance of infections in humans, and animals. The system embraces the IoT <i>“digital by design”</i> metaphor by incorporating elements of connectivity, data sharing and (secure) information arbitrage. Using an array of aptamers to bind viral targets may help in detection, diagnostics, and potentially prevention in case of SARS-CoV-2. The ADD tool may become part of a broader platform approach.</p>

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Artificial-intelligence tools aim to tame the coronavirus literature

Cited by 26 publications

References 0 publications

Scientific globalism during a global crisis: research collaboration and open access publications on COVID-19

Scientific globalism during a global crisis: research collaboration and open access publications on COVID-19

Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature

Aptamers for Detection and Diagnostics (ADD) is a proposed mobile app acquiring optical data from conjugated quantum nanodots to identify molecules indicating presence of SARS-CoV-2 virus: Why public health and healthcare need smartphone sensors as a platform for early detection and prevention

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