Universiteit Leiden
Population Health: Predictive Analytics
Universiteit Leiden

Population Health: Predictive Analytics

Taught in English

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5,057 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Ewout W. Steyerberg
David van Klaveren

Instructors: Ewout W. Steyerberg

4.6

(23 reviews)

Intermediate level

Recommended experience

17 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the role of predictive analytics for prevention, diagnosis, and effectiveness

  • Explain key concepts in prediction modelling: appropriate study design, adequate sample size and overfitting

  • Understand important issues in model development, such as missing data, non-linear relations and model selection

  • Know about ways to assess model quality through performance measures and validation

Details to know

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Assessments

16 quizzes

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There are 5 modules in this course

Welcome to the course Predictive Analytics! We are excited to have you in class and look forward to your contributions to the learning community. To begin, we recommend taking a few minutes to explore the course site. Review the material we will cover each week, and preview the assignments you will need to complete in order to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and we hope you enjoy the course!

What's included

2 videos6 readings2 discussion prompts1 plugin

In this module, we discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. We begin with a brief introduction to predictive analytics, which we follow by differentiating between population-based and targeted interventions. We then explain why and when it may be beneficial to test for a diagnosis, and how analytic tools can help inform these decisions. Finally, we focus on the balance between benefits and harms of a certain treatment, and how we can predict the benefit for an individual.

What's included

6 videos6 quizzes

In this module, we will present some key concepts in prediction modeling. First, we weigh the strengths and weakness of various study designs. Second, we stress the importance of an appropriate sample size for reliable inference. Then, we discuss the issues of overfitting a prediction model, and regression-to-the-mean. Finally, we will guide you through the popular bootstrap procedure, showing how it can be used to assess parameter variability.

What's included

6 videos3 readings3 quizzes1 discussion prompt

In this module, we focus on model development. First, we turn our attention to the missing values problem. We discuss well-known missingness mechanisms, and methods to deal with missing values appropriately. Second, we learn about methods to deal with non-linearity in a dataset. We then address the topic of model selection, focusing on the limitations of traditional stepwise selection procedures. Last, we talk about how introducing bias in exchange for lower variance can improve prediction quality. This can be done by using advanced methods, such as LASSO and Ridge regression.

What's included

6 videos4 readings2 quizzes2 discussion prompts

In this final module, we learn about assessing the quality of a prediction model. First, we extensively discuss standard performance measures for both binary and continuous outcomes. Second, we explore different ways of validating a prediction model. We look at how to assess both the internal, and the more relevant external validity of a model. Next, we will look at how to update a model and make it applicable to a specific medical setting. We conclude with an interview, where we more broadly discuss the potential of predictive analytics by taking the example of the island of Aruba.

What's included

6 videos2 readings5 quizzes2 discussion prompts

Instructors

Instructor ratings
4.9 (11 ratings)
Ewout W. Steyerberg
Universiteit Leiden
1 Course5,057 learners
David van Klaveren
Universiteit Leiden
1 Course5,057 learners

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Recommended if you're interested in Data Analysis

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4.6

23 reviews

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    4.34%

WG
5

Reviewed on Oct 26, 2020

FG
5

Reviewed on Jan 6, 2021

PP
5

Reviewed on Sep 13, 2020

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