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- W4387210464 abstract "The COVID-19 pandemic has globally posed numerous health challenges, notably the emergence of post-COVID-19 cardiovascular complications. This study addresses this by utilizing data-driven machine learning models to predict such complications in 352 post-COVID-19 patients from Iraq. Clinical data, including demographics, comorbidities, lab results, and imaging, were collected and used to construct predictive models. These models, leveraging various machine learning algorithms, demonstrated commendable performance in identifying patients at risk. Early detection through these models promises timely interventions and improved outcomes. In conclusion, this research underscores the potential of data-driven machine learning for predicting post-COVID-19 cardiovascular complications, emphasizing the need for continued validation and research in diverse clinical settings." @default.
- W4387210464 created "2023-10-01" @default.
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- W4387210464 creator A5087258157 @default.
- W4387210464 date "2023-09-27" @default.
- W4387210464 modified "2023-10-17" @default.
- W4387210464 title "Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models" @default.
- W4387210464 doi "https://doi.org/10.48550/arxiv.2309.16059" @default.
- W4387210464 hasPublicationYear "2023" @default.
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