This paper is in the following e-collection/theme issue:

Infectious Diseases (non-STD/STI) (1726) JMIR Theme Issue: COVID-19 Special Issue (2554) Registered Report (791) Machine Learning (2076) Chronic Conditions (434) Electronic Health Records (1319) Secondary Use of Clinical Data for Research and Surveillance (474)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2023.07.27.23293272v1, first published .
Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study

Authors of this article:

Zachary Butzin-Dozier1 Author Orcid Image ;   Yunwen Ji1 Author Orcid Image ;   Haodong Li1 Author Orcid Image ;   Jeremy Coyle1 Author Orcid Image ;   Junming Shi1 Author Orcid Image ;   Rachael V Phillips1 Author Orcid Image ;   Andrew N Mertens1 Author Orcid Image ;   Romain Pirracchio2 Author Orcid Image ;   Mark J van der Laan1 Author Orcid Image ;   Rena C Patel3 Author Orcid Image ;   John M Colford1 Author Orcid Image ;   Alan E Hubbard1 Author Orcid Image ;   The National COVID Cohort Collaborative (N3C) Consortium4
Article Authors Cited by (1) Tweetations (7) Metrics Author Video

Journals

  1. Li S, Zhang Y, Fu K, Fang K, Zheng L, Lin Y, Wu J. Machine learning ensemble meets clinical practice: developing a real-world risk prediction model for metabolic syndrome using super learner and scorecard approaches. Journal of Advanced Research 2025 View