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Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google .
What You Will Learn
- Create a computational phenotyping algorithm
- Assess algorithm performance in the context of analytic goal.
- Create combinations of at least three data types using boolean logic
- Explain the impact of individual data type performance on computational phenotyping.
Introduction: Identifying Patient Populations
Learn about computational phenotyping and how to use the technique to identify patient populations.
Tools: Clinical Data Types
Understand how different clinical data types can be used to identify patient populations. Begin developing a computational phenotyping algorithm to identify patients with type II diabetes.
Techniques: Data Manipulations and Combinations
Learn how to manipulate individual data types and combine multiple data types in computational phenotyping algorithms. Develop a more sophisticated computational phenotyping algorithm to identify patients with type II diabetes.
Techniques: Algorithm Selection and Portability
Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes.
Practical Application: Develop a Computational Phenotyping Algorithm to Identify Patients with Hypertension
Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension.