Predictive Models Using Interpretable Machine Learning for Cardiovascular Survival in Breast Cancer Patients with Secondary Malignancies: Insights from a SEER Analysis - Scorecard - MDSpire
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Predictive Models Using Interpretable Machine Learning for Cardiovascular Survival in Breast Cancer Patients with Secondary Malignancies: Insights from a SEER Analysis
Clinical Scorecard: Predictive Models Using Interpretable Machine Learning for Cardiovascular Survival in Breast Cancer Patients with Secondary Malignancies: Insights from a SEER Analysis
At a Glance
Category
Detail
Condition
Key Mechanisms
Target Population
Breast cancer survivors aged 18 and older with second primary cancers diagnosed between 2010 and 2021
Care Setting
Key Highlights
Machine learning models (RSF, SVM, XGBoost, DeepSurv) outperform traditional statistical methods in predicting cardiovascular survival
Guideline-Based Recommendations
Diagnosis
Management
Implement proactive management strategies for cardiovascular complications, including regular screenings and tailored treatment plans.
Monitoring & Follow-up
Risks
Patient & Prescribing Data
Consideration of treatment modalities including surgery, radiotherapy, and chemotherapy, along with their associated cardiovascular risks.
Clinical Best Practices
Adopt a multidisciplinary approach involving oncologists, cardiologists, and primary care providers to manage both cancer and cardiovascular health.
