Identifying Patient Populations (Coursera)

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.

Syllabus

WEEK 1

Introduction: Identifying Patient Populations

Learn about computational phenotyping and how to use the technique to identify patient populations.

WEEK 2

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.

WEEK 3

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.

WEEK 4

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.

WEEK 5

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.