Correction: Development and validation of an interpretable machine learning model for venous thromboembolism risk prediction in patients with lung cancer: a real-world study - Scorecard - MDSpire
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Correction: Validation and Development of an Explainable Machine Learning Approach for Predicting Venous Thromboembolism Risk in Lung Cancer Patients: A Real-World Analysis
Clinical Scorecard: Correction: Validation and Development of an Explainable Machine Learning Approach for Predicting Venous Thromboembolism Risk in Lung Cancer Patients: A Real-World Analysis
At a Glance
Category
Detail
Condition
Venous Thromboembolism in Lung Cancer Patients
Key Mechanisms
Machine learning and SHapley Additive exPlanation for risk prediction
Target Population
Patients with lung cancer
Care Setting
Real-world clinical analysis
Key Highlights
Correction of P-values for Anticoagulant and Chemotherapy drugs to 1.000
No statistically significant differences in baseline characteristics between cohorts
Model construction and results remain unchanged
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
Patient & Prescribing Data
Lung cancer patients at risk for venous thromboembolism
Utilization of machine learning for risk assessment
