This course teaches you the fundamentals of transforming clinical practice using predictive models. This course examines specific challenges and methods of clinical implementation, that clinical data scientists must be aware of when developing their predictive models.
Overview
Syllabus
- Introduction: Clinical Prediction Models
- Learn about the many types of clinical prediction models that exist and how they are put into practice.
- Tools: Ensuring Model Usability
- Understand how qualitative methods can be used to develop clinical prediction models that are more likely to transform clinical practice.
- Techniques: Model Implementation and Sustainability
- Learn about the different tools that are used to implement clinical prediction models in practice and the factors that affect implementations over time.
- Techniques: Data Selection, Model Building, and Evaluation
- Understand how the different types of clinical data can be used in prediction models and learn how choices made during model construction affect the utility of the model in practice.
- Practical Application: Developing a Clinical Prediction Model
- Put your new skills to the test - develop a clinical prediction model to asses risk of death during a stay in an Intensive Care Unit (ICU) stay.
Taught by
Laura K. Wiley, PhD
