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Learner Reviews & Feedback for Population Health: Predictive Analytics by Universiteit Leiden

5.0
stars
12 ratings
8 reviews

About the Course

Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better. In this course, you will learn how to make accurate prediction tools, and how to assess their validity. First, we will discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. Then, we look at key concepts such as study design, sample size and overfitting. Furthermore, we comprehensively discuss important modelling issues such as missing values, non-linear relations and model selection. The importance of the bias-variance tradeoff and its role in prediction is also addressed. Finally, we look at various way to evaluate a model - through performance measures, and by assessing both internal and external validity. We also discuss how to update a model to a specific setting. Throughout the course, we illustrate the concepts introduced in the lectures using R. You need not install R on your computer to follow the course: you will be able to access R and all the example datasets within the Coursera environment. We do however make references to further packages that you can use for certain type of analyses – feel free to install and use them on your computer. Furthermore, each module can also contain practice quiz questions. In these, you will pass regardless of whether you provided a right or wrong answer. You will learn the most by first thinking about the answers themselves and then checking your answers with the correct answers and explanations provided. This course is part of a Master's program Population Health Management at Leiden University (currently in development)....

Top reviews

PP
Sep 13, 2020

Provide lots of useful tips for practical deployment of predictive analytics and also some brief theoretical background. A very well presented course.

TS
Jan 4, 2021

Helpful course for the ones wanting to discover and understand how predictive analytics can help you in approaching health-related issues.

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1 - 8 of 8 Reviews for Population Health: Predictive Analytics

By Utibe S E

Jun 11, 2020

Great content!

By Sergio U

Oct 4, 2020

Great course. It goes from the basics and gradually introduces new concepts. At the beginning of the statistical part, certain knowledge is assumed, especially of regression. I took this course to be able to study the Clinical Prediction Models textbook in detail and I feel that I learned the basic vocabulary and key terms to be able to start studying. Professor Steyerberg's videos and explanations are clear, short, and direct and the effort to simplify a complex subject is appreciated. It is a fully recommended course especially now that many articles with predictive models have begun to appear, many of which lack the essential methodological elements discussed in this course.

By Dwayne R T

Feb 8, 2021

The course was quite detailed; it covered a wide range of topics within the shortest possible time-span. The instructors provided a surplus of study and practice material and suggestions. Thoroughly enjoyed the content as it is quite relevant to my current work as a PhD student.

By PC

Sep 14, 2020

Provide lots of useful tips for practical deployment of predictive analytics and also some brief theoretical background. A very well presented course.

By Thomas S

Jan 5, 2021

Helpful course for the ones wanting to discover and understand how predictive analytics can help you in approaching health-related issues.

By Willem G

Oct 27, 2020

Truly one of few MOOCS that is challenging, providing useful knowledge and instruction.

By Tohaku

Feb 12, 2021

A great overview of predictive analytics applied to clinical settings.

By Fadi G

Jan 7, 2021

Very Challenging and instructive enjoyed it thank you