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Identifying Patient Populations

Identifying Patient Populations

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9.2/10 (Our Score)
Product is rated as #19 in category Data Science

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.

Instructor Details

Dr. Wiley develops methods for using data from electronic health records for precision medicine discovery and implementation. She developed computational phenotyping algorithms for use in EHR-linked biobanks, investigated new algorithms for precision dosing of warfarin in African Americans, and has served as the lead informatician on an NIH Cancer Moonshot-funded project to create a comprehensive tobacco cessation service at the University of Colorado Cancer Center. She is a principle investigator in the Colorado Center for Personalized Medicine and an Assistant Professor in the Division of Biomedical Informatics and Personalized Medicine at the University of Colorado Anschutz Medical Campus. She has served as an ex officio member of the American Medical Informatics Association Board of Directors and is Vice-Chair of the AMIA 2019 Annual Symposium.

Specification: Identifying Patient Populations

Duration

19 hours

Year

2019

Level

Intermediate

Certificate

Yes

Quizzes

Yes

8 reviews for Identifying Patient Populations

4.4 out of 5
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  1. William H

    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.

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  2. Angela B

    This is a well–presented course. I highly recommend.

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  3. qianmengxiao

    excellent course. The first MOOC on computational pheonotying

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  4. Mor K

    Great course, gives a solid understanding of computational phenotyping. Also teaches some R programming!

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  5. Vu T T T

    A good course on identifying patient populations with R. However, some concepts/use of functions in R programming do not get fully–explained and needs learnt from other sources. Courses 4, 5 and 6 are not available so cannot complete the entire specialization.

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  6. rroddema

    I am simply not able to finish the course because no peer reviewers available. Not sure who is responsible but as a student I do not care. Please stop asking money when you cannot deliver. Course content is very good.

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  7. Kazuki Y

    Good course material for studying patient selection methods.

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  8. Fidel G

    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.

    Helpful(0) Unhelpful(0)You have already voted this

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