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. 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 Cloud.
Этот курс входит в специализацию ''Специализация Clinical Data Science'
от партнера
Специализация Clinical Data ScienceСистема университетов штата КолорадоОб этом курсе
Some programming experience in any language.
Чему вы научитесь
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.
Some programming experience in any language.
от партнера
Система университетов штата Колорадо
The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond.
Программа курса: что вы изучите
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.
Рецензии
- 5 stars70,58 %
- 4 stars17,64 %
- 3 stars2,94 %
- 1 star8,82 %
Лучшие отзывы о курсе IDENTIFYING PATIENT POPULATIONS
The instructor does a great job of providing hands-on teaching in addition to lecture. However, this course required a lot of knowledge of R, which wasn't provided in the introductory course.
Great overview of how to identify Patient Population and the in and out of what to look for when you are thinking about your potential research project will involve.
Great course, gives a solid understanding of computational phenotyping. Also teaches some R programming!
This is a well-presented course. I highly recommend.
Специализация Clinical Data Science: общие сведения
Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist!
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Можно ли получить финансовую помощь?
I live in an area that restricts access to Google products. Will I be able to complete the specialization?
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