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

Yueh-Chen Hsieh¹Sin Chen²Shu-Yu Tsao¹Jiun-Ruey Hu³Wan-Ting Hsu⁴Chien-Chang Lee^5*

• ¹National Taiwan University Hospital, Taipei, Taiwan
• ²National Taiwan University, Taipei, Taiwan
• ³Cedars-Sinai Medical Center, Los Angeles, United States
• ⁴Harvard T.H. Chan School of Public Health, Boston, United States
• ⁵Department of Information Management, Ministry of Health and Welfare, Taipei, Taiwan

ABSTRACT Background This study aims to develop a machine learning model to predict the 30-day mortality risk of hospitalized COVID-19 patients while leveraging federated learning to enhance data privacy and expand the model's applicability. Additionally, SHapley Additive exPlanations (SHAP) values were utilized to assess the impact of comorbidities on mortality. Methods A retrospective analysis was conducted on 6,321 clinical records of hospitalized COVID-19 patients between January 2021 and October 2022. After excluding cases involving patients under 18 years of age and non-Omicron infections, a total of 4,081 records were analyzed. Key features included three demographic data, six vital signs at admission, and 79 underlying comorbidities. Four machine learning models were compared, including Lasso, Random Forest, XGBoost, and TabNet, with XGBoost demonstrating superior performance. Federated learning was implemented to enable collaborative model training across multiple medical institutions while maintaining data security. SHAP values were applied to interpret the contribution of each comorbidity to the model's predictions. Results A subset of 2,156 records from the Taipei branch was used to evaluate model performance. XGBoost achieved the highest AUC of 0.96 and a sensitivity of 0.94. Two versions of the XGBoost model were trained: one incorporating vital signs, suitable for emergency room applications where patients come in with unstable vital signs, and another excluding vital signs, optimized for outpatient settings where we encounter patients with multiple comorbidities. Federated learning was then applied to refine the final models using data from three hospital branches. SHAP analysis identified key comorbidities influencing 30-day mortality, providing insights into potential risk factors. Conclusion 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.