ArticleEBioMedicine2023
Predictive models of long COVID.
Article in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- The prolonged health sequelae "of the COVID-19 pandemic" in sub-Saharan Africa: a systematic review and meta-analysis.Frontiers in public health · 2025Pooled it
- Post-COVID-19 Condition Diagnosis among Older People: Findings from Swedish National Register Data.Gerontology · 2026Article
- Long COVID and Its Impact on Daily Functioning: Findings from the Si-Panda Behavioural Insights Survey in Slovenia.Zdravstveno varstvo · 2026Article
- Associated factors and predictive nomogram of long COVID: a cross-sectional study in China.BMC pulmonary medicine · 2026Article
- Long-term trends in Post-COVID severity: a machine learning analysis from the POP/COVIDOM cohort of the German NAPKON Cohort Network.EClinicalMedicine · 2026Article
- Article
- Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors.Communications medicine · 2026Article
- MTHFR allele and one-carbon metabolic profile predict severity of COVID-19.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Predictive Model for the Development of Long COVID in Children.International journal of environmental research and public health · 2025Article
- Development and internal validation of a prediction model for post-COVID-19 condition 2 years after infection-results of the CORFU study.Diagnostic and prognostic research · 2025Article
- Postacute Sequelae From SARS-CoV-2 at the University of Illinois Hospital and Clinics: An Examination of the Effects of Long COVID in an Underserved Population Utilizing Manual Extraction of Electronic Health Records.American journal of medicine open · 2025Article
- A Bayesian Survival Analysis on Long COVID and Non-Long COVID Patients: A Cohort Study Using National COVID Cohort Collaborative (N3C) Data.Bioengineering (Basel, Switzerland) · 2025Article
- Identifying risk factors and predicting long COVID in a Spanish cohort.Scientific reports · 2025Article
- Wearable data reveals distinct characteristics of individuals with persistent symptoms after a SARS-CoV-2 infection.NPJ digital medicine · 2025Article
- Association of systemic inflammation and long-term dysfunction in COVID-19 patients: A prospective cohort.Psychoneuroendocrinology · 2025Article
- Relevance of superoxide dismutase type 1 to lipoid pneumonia: the first retrospective case-control study.Respiratory research · 2025Observational
- An ecological comparison to inspect the aftermath of post COVID-19 condition in Italy and the United States.Scientific reports · 2024Article
- Clinical Features and Vaccination Effects among Children with Post-Acute Sequelae of COVID-19 in Taiwan.Vaccines · 2024Article
- Psychological factors associated with Long COVID: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Chronic Lung Disease as a Risk Factor for Long COVID in Patients Diagnosed With Coronavirus Disease 2019: A Retrospective Cohort Study.Open forum infectious diseases · 2024Article
Corrections and comments
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Authors and funding
14 authors at 11 institutions in 2 countries.
Funding
Abstract
backgroundThe cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.
methodsWe used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741).
findingsLR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75.
interpretationML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.
fundingNCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.
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