Identifying Patient Populations (Coursera)

Identifying Patient Populations (Coursera)
Course Auditing
Categories
Effort
Certification
Languages
Some programming experience in any language.
Misc

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Identifying Patient Populations (Coursera)
This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

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.



0
No votes yet

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing
39.00 EUR/month
Some programming experience in any language.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.