Web-Based Explainable Machine Learning for Predicting Sepsis in Hemorrhagic Stroke: Creation and Multicenter Validation
By
Duobin Zhang
Liangfang Liu
Shasha Zhang
Zhen Xiao
Xi Chen
Shen Yang
July 18, 2026
Clinical Scorecard: Web-Based Explainable Machine Learning for Predicting Sepsis in Hemorrhagic Stroke: Creation and Multicenter Validation
At a Glance
Category Detail
Condition Hemorrhagic Stroke Key Mechanisms Stroke-induced immunosuppression and increased susceptibility to infections. Target Population ICU patients with hemorrhagic stroke. Care Setting Neurocritical care settings.
Key Highlights
Sepsis is a severe complication in hemorrhagic stroke patients. Machine learning models can improve early prediction of sepsis. Integration of multidimensional electronic health record data enhances predictive accuracy. Explainable AI techniques improve clinical interpretability of risk predictions. The study utilized multicenter databases for model validation. Guideline-Based Recommendations
Diagnosis
Diagnosis of sepsis guided by Sepsis-3 definitions. Management
Implement targeted preventive strategies for high-risk patients. Monitoring & Follow-up
Monitor for signs of infection and organ dysfunction in HS patients. Risks
Increased risk of hospital-acquired infections and sepsis. Patient & Prescribing Data
Patients with hemorrhagic stroke in intensive care.
Invasive life-support therapies increase infection risk.
Clinical Best Practices
Utilize machine learning for risk stratification in neurocritical care. Incorporate explainable AI for better clinical decision-making. Related Resources & Content
