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. This dataset contains the source code (python and shell scripts) used to create the Long Covid collection, along with a snapshot of processed data and predictions.

This research was supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health.