Name: Population Health: Predictive Analytics
Brand: Universiteit Leiden
Availability: OnlineOnly
Rating: 4.6 (11 reviews)

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学生对 Universiteit Leiden 提供的 Population Health: Predictive Analytics 的评价和反馈

4.6

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24 个评分

课程概述

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)....

1 - Population Health: Predictive Analytics 的 11 个评论（共 11 个）

创建者 Utibe S E

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Jun 11, 2020

Great content!

创建者 Sergio U

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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.

创建者 Dwayne R T

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Feb 7, 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.

创建者 PC

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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.

创建者 Thomas S

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Jan 5, 2021

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

创建者 Willem G

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Oct 27, 2020

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

创建者 Hideaki M

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Feb 12, 2021

A great overview of predictive analytics applied to clinical settings.

创建者 Fadi G

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Jan 6, 2021

Very Challenging and instructive enjoyed it thank you

创建者 Meslian M

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Sep 24, 2023

SUPER

创建者 Phil Y

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Jan 2, 2022

There is a Lot of concepts! It needs more hand-on practice and more indept discussion. I think the class should be extended for 2-3 more weeks to better enhance students' skills and understanding of the complex topics.