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Predictive models of long COVID.

Blessy Antony¹, Hannah Blau², Elena Casiraghi³

¹Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, 24061, USA.

|September 6, 2023

Summary

This summary is machine-generated.

Machine learning models effectively predict long COVID using electronic health records. Key predictors include demographics, symptoms, and medications during acute infection, aiding early identification.

Keywords:

COVID-19 Classification Cross-site analysis Explainability Long COVID

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Area of Science:

Medical Informatics
Computational Biology
Epidemiology

Background:

Long COVID's causes and symptoms remain unclear, making future prediction difficult.
Identifying at-risk COVID-19 patients early is crucial for timely intervention.

Purpose of the Study:

To develop and evaluate machine learning models for predicting long COVID incidence.
To identify key clinical and demographic factors associated with long COVID development.

Main Methods:

Utilized National COVID Cohort Collaborative electronic health record (EHR) data.
Trained logistic regression (LR) and random forest (RF) machine learning models.
Included features such as acute infection symptoms, medications, comorbidities, and demographics; long COVID defined by U09.9 ICD10-CM code.

Main Results:

LR and RF models achieved median AUROC scores of 0.76 and 0.75, respectively.
Medications significantly influenced prediction accuracy; SHAP analysis highlighted age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease.
Cross-site validation demonstrated model generalizability with an average AUROC of 0.75.

Conclusions:

Machine learning classification using acute infection EHR data is effective for predicting long COVID.
SHAP analysis identified critical predictive features, offering insights into long COVID risk factors.
The developed methodology shows promise for broad application across different healthcare systems.