Machine Learning-Based Patient Risk Stratification for Healthcare Management
Keywords:
Machine Learning, Patient Risk Stratification, Healthcare Management, Electronic Health RecordsAbstract
This paper explores the application of machine learning (ML) algorithms for patient risk stratification in healthcare management. Patient risk stratification aims to categorize patients into different risk groups based on their health status and predicted outcomes. ML models, including logistic regression, random forest, and gradient boosting, are trained on electronic health record (EHR) data to predict patient risk. The study evaluates the performance of these models and discusses their potential impact on healthcare management. Results show that ML-based risk stratification can improve the efficiency and effectiveness of healthcare delivery by enabling more targeted interventions and resource allocation.
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