Course

Population Health: Predictive Analytics

Universiteit Leiden

Predictive analytics has a longstanding tradition in medicine and is crucial for improving healthcare decision-making. This course delves into the development of accurate prediction tools and the assessment of their validity. Key concepts covered include prediction modeling, study design, sample size, model development issues, and model validation and updating.

  • Understand the role of predictive analytics for prevention, diagnosis, and effectiveness
  • Explain key concepts in prediction modeling
  • Understand important issues in model development
  • Know about ways to assess model quality through performance measures and validation

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Population Health: Predictive Analytics
Course Modules

This course is divided into modules that cover topics such as predictive analytics in prevention, diagnosis, and effectiveness, modeling concepts, model development, and model validation and updating. Each module includes assignments, interactive introductions, and quizzes for comprehensive learning.

Welcome to Leiden University

Welcome to the course Predictive Analytics, where you will gain insights into the role of predictive analytics in medicine and learn about study design, sample size, overfitting, and more. Meet the instructors, explore the glossary, and set your learning goals to begin your journey.

Prediction for prevention, diagnosis, and effectiveness

This module delves into predictive analytics for prevention, diagnosis, and effectiveness. You will learn about prediction modeling, and participate in assignments focusing on prevention, diagnosis, and intervention. Engage in reflective exercises and test your knowledge through quizzes.

Modeling Concepts

Explore modeling concepts in this module, covering study design, sample size, overfitting, and bootstrapping. Engage in interactive introductions and assignments to solidify your understanding of these key concepts. Test your knowledge through quizzes and reflective exercises.

Model development

Discover model development principles, including handling missing values, non-linear relations, model selection, and estimation. Engage in practical exercises and explore topics such as bias, precision, and imputation of missing values. Participate in quizzes and reflective exercises to reinforce your learning.

Model validation and updating

This module focuses on model validation and updating, covering performance measures, validation approaches, and updating strategies. Dive into case studies and practical exercises, and prepare for a final assessment to evaluate your knowledge and skills.

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