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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Predicting COVID-19 Mortality Risk Among Cardiovascular Disease Patients Using Artificial Intelligence Algorithms: A

¹Social Determinants of Health Research Center Semnan University of Medical Sciences Semnan Iran.

Health Science Reports

|April 8, 2026

Summary

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This study developed an AI model to predict COVID-19 mortality in cardiovascular disease (CVD) patients. The XGBoost model accurately identified high-risk individuals, aiding resource allocation and improving patient outcomes.

Area of Science:

Artificial Intelligence in Medicine
Cardiovascular Disease Research
Infectious Disease Epidemiology

Background:

COVID-19 poses severe risks to patients with cardiovascular disease (CVD).
Accurate prediction of COVID-19 mortality in CVD patients is essential for effective management.
Transitioning from pandemic to endemic necessitates understanding disease impact on vulnerable populations.

Purpose of the Study:

To develop and evaluate AI-based predictive models for COVID-19 mortality risk.
To identify key predictors of mortality in CVD patients with COVID-19.
To enhance clinical decision-making and resource allocation for at-risk populations.

Main Methods:

Retrospective analysis of 1255 CVD patients admitted with COVID-19.

Related Experiment Videos

Last Updated: Apr 9, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Utilized demographics, clinical history, features, and lab findings to train AI models.

Employed SHAP (SHapley Additive exPlanations) for AI explainability (XAI).

Main Results:

The XGBoost (XGB) model demonstrated superior predictive performance (AUC=0.864, Accuracy=0.929).
Key predictors identified include age, pneumonia, ICU admission, surgery type, and d-dimer levels.
SHAP values provided insights into the model's decision-making process.

Conclusions:

The XGBoost model effectively stratifies COVID-19 mortality risk in CVD patients.
Improved risk stratification enables better clinical resource allocation and patient prognosis.
The AI model offers significant predictive performance and clinical utility for managing COVID-19 in CVD populations.