Predictive Analytics & Clinical Decision Support.

Catalog description

Students explore advanced techniques for statistical modeling and risk prediction, applying them to predict patient outcomes, identify high-risk individuals, and guide clinical interventions. They study regression models, decision trees, random forests, and neural networks, using these methods in clinical scenarios. Students also examine how predictive models integrate with clinical decision support tools to deliver real-time recommendations for healthcare professionals, ultimately improving decision-making, reducing errors, and enhancing patient care. Prerequisite: ABMS 5301 , ABMS 5302 , ABMS 5303 & ABMS 5304 .