Using machine learning methods to investigate the impact of comorbidities and clinical indicators on the mortality rate of COVID-19

XGBoost outperformed other models making it a viable tool for both emergency and outpatient settings. The study underscores the importance of chronic disease assessment in predicting COVID-19 mortality, revealing some comorbidities such as diabetes mellitus, cerebrovascular disease and chronic lung disease to have protective association with 30-day mortality. These findings suggest potential refinements in current treatment guidelines, particularly concerning high-risk conditions. The integration of federated learning further enhances the model's clinical applicability while preserving patient privacy.

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Publication Details

Journal
Frontiers in Medical Technology
Published
2025-09-22
DOI
https://doi.org/10.3389/fmedt.2025.1621158
Citations
1
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.36

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article

Using machine learning methods to investigate the impact of comorbidities and clinical indicators on the mortality rate of COVID-19

Ya‐Ju Hsieh, S.Y. Tsao, Chien‐Chang Lee, Jiun‐Ruey Hu et al.
1 citations
Frontiers in Medical Technology
COVID-19 diagnosis using AI
1.36
article

Using machine learning methods to investigate the impact of comorbidities and clinical indicators on the mortality rate of COVID-19

Ya‐Ju Hsieh, S.Y. Tsao, Chien‐Chang Lee, Jiun‐Ruey Hu, Wan‐Ting Hsu, S.-A. Chen
article en
1 citations

Abstract

XGBoost outperformed other models making it a viable tool for both emergency and outpatient settings. The study underscores the importance of chronic disease assessment in predicting COVID-19 mortality, revealing some comorbidities such as diabetes mellitus, cerebrovascular disease and chronic lung disease to have protective association with 30-day mortality. These findings suggest potential refinements in current treatment guidelines, particularly concerning high-risk conditions. The integration of federated learning further enhances the model's clinical applicability while preserving patient privacy.

Frontiers in Medical TechnologyVol. 7
Cedars-Sinai Medical Center (US), Harvard University (US), National Taiwan University (TW), Ministry of Health and Welfare (TW), National Taiwan University Hospital (TW), Cedars-Sinai Smidt Heart Institute (US), Brookline Public Schools (US)
National Taiwan University Hospital, National Taiwan University, National Science and Technology Council, National Taiwan University Hospital Yunlin Branch
Openalex Percentile: Top 15%
COVID-19 diagnosis using AI
1.36
DOIOpen Access PDFOpenAlex
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Using machine learning methods to investigate the impact of comorbidities and clinical indicators on the mortality rate of COVID-19 — Ya‐Ju Hsieh, S.Y. Tsao, et al. · Frontiers in Medical Technology (2025) | TGRS Research Map | TGRS