Predicting the Utility of Scientific Articles for Emerging Pandemics Using Their Titles and Natural Language Processing.

Abstract

Objective: Not all scientific publications are equally useful to policy-makers tasked with mitigating the spread and impact of diseases, especially at the start of novel epidemics and pandemics. The urgent need for actionable, evidence-based information is paramount, but the nature of preprint and peer-reviewed articles published during these times is often at odds with such goals. For example, a lack of novel results and a focus on opinions rather than evidence were common in coronavirus disease (COVID-19) publications at the start of the pandemic in 2019. In this work, we seek to automatically judge the utility of these scientific articles, from a public health policy making persepctive, using only their titles.

Methods: Deep learning natural language processing (NLP) models were trained on scientific COVID-19 publication titles from the CORD-19 dataset and evaluated against expert-curated COVID-19 evidence to measure their real-world feasibility at screening these scientific publications in an automated manner.

Results: This work demonstrates that it is possible to judge the utility of COVID-19 scientific articles, from a public health policy-making perspective, based on their title alone, using deep natural language processing (NLP) models.

Conclusions: NLP models can be successfully trained on scienticic articles and used by public health experts to triage and filter the hundreds of new daily publications on novel diseases such as COVID-19 at the start of pandemics.