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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Clinical Data Science
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Spezialisierung Clinical Data ScienceUniversity of Colorado SystemÜber diesen Kurs
Some programming experience in any language.
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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.
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Lehrplan - Was Sie in diesem Kurs lernen werden
Introduction: Identifying Patient Populations
Tools: Clinical Data Types
Techniques: Data Manipulations and Combinations
Techniques: Algorithm Selection and Portability
Bewertungen
- 5 stars71,42 %
- 4 stars17,14 %
- 3 stars2,85 %
- 1 star8,57 %
Top-Bewertungen von IDENTIFYING PATIENT POPULATIONS
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.
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.
This is a well-presented course. I highly recommend.
Über den Spezialisierung Clinical Data Science
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