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Population Health: Predictive Analytics
  • Level Professional
  • المدة 17 ساعات hours
  • الطبع بواسطة Universiteit Leiden
  • Offered by

عن

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).

الوحدات

Welcome to the Predictive Analytics

    • 2 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Plugin
Show info about module content

1 Discussions

  • Introduce yourself

2 Videos

  • Welcome to the course Predictive Analytics
  • How to succeed in your online class?

3 Readings

  • Meet the instructors & the team
  • About this course
  • Glossary

Introduction

    • 1 Videos
Show info about module content

1 Videos

  • Introduction

Screening and diagnosis

    • 3 Videos
    • 3 Assignment
Show info about module content

3 Assignment

  • Introductory assignment
  • Prevention assignment
  • Diagnosis assignment

3 Videos

  • Introduction to predictive analytics
  • Predictive analytics in prevention
  • Predictive analytics diagnosis

Treatment

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Intervention assignment

1 Videos

  • Predictive analytics in intervention

To conclude

    • 1 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Reflect on your goals
  • Test your knowledge

1 Videos

  • To conclude

Introduction

    • 1 Videos
Show info about module content

1 Videos

  • Introduction

Data sources for prediction

    • 2 Videos
    • 2 Readings
Show info about module content

2 Videos

  • Design issues
  • Sample size

2 Readings

  • Is caring about measurement error an error?
  • Sample size

Predict the past

    • 1 Videos
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Testimation bias - an interactive introduction

1 Discussions

  • Winner's curse

1 Videos

  • Overfitting

Predicting the future

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Bootstrapping

1 Readings

  • Bootstrapping 101 in R

To conclude

    • 1 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Reflect on your goals
  • Test your knowledge

1 Videos

  • To conclude

Introduction

    • 1 Videos
Show info about module content

1 Videos

  • Introduction

Missing values

    • 1 Videos
    • 1 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Multiple imputation: potential and pitfalls

1 Videos

  • Missing values

1 Readings

  • Bias, precision and simple imputation of missing values

Flexible functions modeling

    • 1 Videos
    • 1 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • 'Dichotomania' @Twitter

1 Videos

  • Continuous predictors

1 Readings

  • Dealing with non-linearity

Model selection

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Model selection

1 Readings

  • Model selection

Model estimation

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Model estimation

1 Readings

  • Model estimation

To conclude

    • 1 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Reflect on your goals
  • Test your knowledge

1 Videos

  • To conclude

Introduction

    • 1 Videos
Show info about module content

1 Videos

  • Introduction

Assessing Quality

    • 2 Videos
    • 2 Readings
    • 3 Assignment
Show info about module content

3 Assignment

  • Recall - Performance I
  • Recall - Performance I
  • Validation cardiovascular disease

2 Videos

  • Performance measures
  • Validation approaches

2 Readings

  • Performance I - Statistical measures
  • Performance II - Evaluation of usefulness

Improving your model

    • 2 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Aruba

2 Videos

  • Updating approaches
  • Predictive analytics for Aruba

To conclude

    • 2 Assignment
Show info about module content

2 Assignment

  • Reflect on your goals
  • Test your knowledge

To conclude this course

    • 1 Videos
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Final Assessment

1 Discussions

  • Share your glossary

1 Videos

  • To conclude

Auto Summary

Explore the critical role of predictive analytics in healthcare with "Population Health: Predictive Analytics," a professional-level course offered by Coursera. Led by expert instructors from Leiden University, this course delves into the creation and validation of prediction models, covering essential topics such as study design, sample size, overfitting, and the bias-variance tradeoff. Utilizing R for practical applications, learners will gain hands-on experience without needing prior installation. Ideal for those in the data science and AI domain, this 1020-minute course includes practice quizzes and is available through a Starter subscription.

Ewout W. Steyerberg
Instructors

Ewout W. Steyerberg

David van Klaveren
Instructors

David van Klaveren