Comprehensively identifying Long Covid articles with human-in-the-loop machine learning

Robert Leaman; Rezarta Islamaj; Alexis Allot; Qingyu Chen; W John Wilbur; Zhiyong Lu

doi:10.1016/j.patter.2022.100659

Comprehensively identifying Long Covid articles with human-in-the-loop machine learning

Patterns (N Y). 2023 Jan 13;4(1):100659. doi: 10.1016/j.patter.2022.100659. Epub 2022 Dec 1.

Authors

Robert Leaman¹, Rezarta Islamaj¹, Alexis Allot¹, Qingyu Chen¹, W John Wilbur¹, Zhiyong Lu¹

Affiliation

¹ National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health 8600 Rockville Pike, Bethesda, MD 20894, USA.

Abstract

A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid Collection shows that (1) most Long Covid articles do not refer to Long Covid by any name, (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid Collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid.

Keywords: COVID-19; Long Covid; active learning; data programming; machine learning; natural language processing; post-acute sequelae of SARS-CoV-2 infection; text classification; weak supervision.