  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
