Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
This work addresses the problem of efficiently finding relevant Long Covid literature for researchers and clinicians, though it is incremental as it applies existing methods to a new domain.
The researchers tackled the challenge of identifying scientific articles about Long Covid due to inconsistent terminology by developing a human-in-the-loop machine learning framework, which achieved higher specificity and sensitivity than other methods.
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