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Population Health: Predictive Analytics に戻る

ライデン大学(Universiteit Leiden) による Population Health: Predictive Analytics の受講者のレビューおよびフィードバック

4.9
15件の評価
8件のレビュー

コースについて

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)....

人気のレビュー

PP
2020年9月13日

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

TS
2021年1月4日

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

フィルター:

Population Health: Predictive Analytics: 1 - 8 / 8 レビュー

by Utibe S E

2020年6月11日

Great content!

by Sergio U

2020年10月4日

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

2021年2月8日

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

2020年9月14日

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

2021年1月5日

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

by Willem G

2020年10月27日

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

by Tohaku

2021年2月12日

A great overview of predictive analytics applied to clinical settings.

by Fadi G

2021年1月7日

Very Challenging and instructive enjoyed it thank you