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

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, iden-tifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus ter-minology. We developed an iterative human-in-the-loop machine learning framework combining data program-ming 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 ar-ticles 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.

Automated summary

A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms known as Long Covid. Identifying relevant scientific articles on Long Covid is challenging due to lack of standardized terminology. A machine learning framework combining data programming with active learning shows higher specificity and sensitivity compared to other methods. Analysis of the Long Covid Collection reveals that most articles do not refer to Long Covid by any name, and when mentioned, Long Covid is the most frequently used term associated with disorders in various body systems. The Long Covid Collection is regularly updated and searchable on the LitCovid portal.