Online explainable machine learning prediction of sepsis in hemorrhagic stroke: Development and multicenter external validation
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By
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Duobin Zhang
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Liangfang Liu
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Shasha Zhang
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Zhen Xiao
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Xi Chen
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Shen Yang
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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.
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