Machine Learning Approaches for Mortality Risk Assessment in Elderly COVID-19 Patients: Identifying Key Immunological Biomarkers and Dose-Response Relationships
By
Lin Luo
Lin Wang
Hao Wang
Hui Li
Ting Liu
Sha Yu
May 25, 2026
Clinical Scorecard: Machine Learning Approaches for Mortality Risk Assessment in Elderly COVID-19 Patients: Identifying Key Immunological Biomarkers and Dose-Response Relationships
At a Glance
Category Detail
Condition COVID-19 mortality risk in elderly patients Key Mechanisms Utilization of routine hematological indicators for risk prediction Target Population Elderly COVID-19 patients Care Setting Clinical settings requiring early risk stratification
Key Highlights
2393 COVID-19 patients were analyzed to develop a machine learning model. The LGBM model achieved an AUC of 0.973 and a recall of 0.924. Top 10 key features for mortality risk included CRP, D-dimer, and age. Non-linear associations observed with CRP and D-dimer levels. A simplified model reduced training time by 58.31% without compromising performance. Guideline-Based Recommendations
Diagnosis
Utilize routine hematological indicators for initial assessment. Management
Implement machine learning models for early risk stratification. Monitoring & Follow-up
Regularly assess key biomarkers such as CRP and D-dimer. Risks
Older adults are at significantly higher risk of COVID-19 mortality. Patient & Prescribing Data
Elderly patients with COVID-19
Focus on monitoring hematological indicators to guide treatment decisions.
Clinical Best Practices
Incorporate machine learning models in clinical practice for risk assessment. Prioritize resource allocation based on mortality risk predictions. Related Resources & Content
