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.
This is a well-presented course. I highly recommend.
I was able to lean many aspects of Patient populations and gain R programming knowledge
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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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.
Don't believe what coursera says
Coursera advertised the course as "at your own place" what a lie. After completing the course I had to pay another $100 just to wait for the final assignment to be marked to get my certificate.
The courses for the specialisation keep getting pushed back, so you have to shell out a subscription for another month while you wait for them to come out.
I've spent far more time paying just to wait than actually doing any of the course materials.
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.
Good course with details on each and every steps we perform to build and case and control cohorts to test clinical hypothesis in space of diagnosis,prognosis or treatment. Thanks a lot for helping us to learn and upgrade in short period of time.
Great course, gives a solid understanding of computational phenotyping. Also teaches some R programming!
I was able to lean many aspects of Patient populations and gain R programming knowledge
Good course material for studying patient selection methods.
excellent course.
The first MOOC on computational pheonotying
This is a well-presented course. I highly recommend.
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.
Do not take. There is no student population in this course and you could be waiting near a month for your final assignment to be graded