Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.
Patterns (N Y)
; : 100659, 2022 Dec 01.
Article
in English
| MEDLINE | ID: covidwho-2242541
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Topics:
Long Covid
Language:
English
Journal:
Patterns (N Y)
Year:
2022
Document Type:
Article
Affiliation country:
J.patter.2022.100659
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