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

TL;DR

This paper presents a human-in-the-loop machine learning framework that effectively identifies Long Covid articles despite inconsistent terminology, aiding researchers in tracking and understanding the condition.

Contribution

It introduces an iterative ensemble model combining data programming and active learning to improve detection of Long Covid literature with high sensitivity and specificity.

Findings

Most Long Covid articles do not explicitly mention Long Covid.

The term 'Long Covid' is the most frequently used name when the condition is named.

Long Covid articles are associated with diverse body system disorders.

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