Population Health: Predictive Analytics from Coursera

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).

What's inside

Syllabus

Welcome to Leiden University

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!

Prediction for prevention, diagnosis, and effectiveness

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.

Modeling Concepts

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.

Model development

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.

Model validation and updating

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to the fundamentals of predictive analytics and its role in healthcare

Helps learners develop skills in building and evaluating prediction models, which are essential for guiding decision-making in healthcare

Uses R for illustration, which is a widely used programming language in data analysis and machine learning

Provides practice quiz questions to reinforce learning

Aligned with the Master's program in Population Health Management at Leiden University

Reviews summary

Predictive analytics in healthcare

Learners say this engaging course, titled Population Health: Predictive Analytics, provides a highly useful overview of applying predictive analytics and machine learning to clinical settings. Many students note that the course is rigorous and challenging, and that it assumes some background or prerequisite knowledge of statistics, especially regression. Despite these challenges, students are very satisfied overall with the range of topics covered and the course's clear and helpful explanations.

Course provides lots of useful study and practice material.

"The instructors provided a surplus of study and practice material and suggestions."

Good preparation for the Clinical Prediction Models textbook.

"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."

Rigorous and challenging course.

"Very Challenging and instructive enjoyed it thank you"

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

Assumes some prior knowledge of statistics and regression.

"At the beginning of the statistical part, certain knowledge is assumed, especially of regression."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Population Health: Predictive Analytics with these activities:

Introduction to Statistical Learning

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This book provides a comprehensive overview of statistical learning techniques

View An Introduction to Statistical Learning: with... on Amazon

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Read the book
Take notes
Complete the exercises

Machine Learning with R

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These tutorials will provide hands-on experience with machine learning techniques

Browse courses on Learning

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Find a tutorial
Follow the steps in the tutorial

Discuss Prediction Models

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Discussing models with others helps reinforce understanding

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Find a peer to discuss with
Choose a prediction model to discuss
Prepare questions and discussion points
Meet with your peer and discuss the model

Two other activities

Expand to see all activities and additional details

Show all five activities

Predict Health Outcomes

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Predictive analytics is a key concept in the course

Browse courses on Prediction

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Gather data on health outcomes
Clean and prepare the data
Select and train a prediction model
Evaluate the model
Deploy the model

Predictive Analytics Hackathon

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This hackathon will provide an opportunity to apply predictive analytics to real-world problems

Show steps

Register for the hackathon
Form a team
Develop a predictive analytics solution
Present your solution to a panel of judges

Career center

Learners who complete Population Health: Predictive Analytics will develop knowledge and skills that may be useful to these careers:

Predictive Modeling Analyst

Predictive Modeling Analysts use statistical and machine learning techniques to build and evaluate predictive models. This course is a perfect fit for those seeking to become Predictive Modeling Analysts as it provides a comprehensive overview of predictive analytics. It covers the entire process, from study design and model development to model validation and updating. The hands-on experience in using R for predictive modeling further enhances the relevance of this course for Predictive Modeling Analysts.

See salaries and explore the career path for Predictive Modeling Analyst

Data Scientist

Data Scientists model data to uncover meaningful information and drive insights that can improve business decision making. Population Health: Predictive Analytics is a comprehensive course that delves into predictive analytics, a key skillset for Data Scientists. It covers study design, modeling concepts, model development, and model validation – all essential components for building and evaluating predictive models. By developing a solid understanding of these concepts, learners can enhance their ability to extract valuable insights from data, which is crucial for a successful career as a Data Scientist.

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Statistician

Statisticians collect, analyze, interpret, and present data to provide insights into various fields. This course is an excellent fit for aspiring Statisticians, particularly those interested in predictive analytics. It covers key concepts in statistical modeling, including study design, model development, and model validation. The emphasis on evaluating model validity and updating models is particularly relevant for Statisticians, as they need to ensure the accuracy and reliability of their statistical findings.

See salaries and explore the career path for Statistician

Health Data Analyst

Health Data Analysts analyze and present health-related data to identify trends, patterns, and insights that drive decision-making. This course is an excellent fit as it equips learners with the skills to apply predictive analytics to health data. Modules on model development and validation provide learners with hands-on experience in building and evaluating predictive models, which is essential for Health Data Analysts seeking to contribute to the improvement of healthcare delivery.

See salaries and explore the career path for Health Data Analyst

Health Statistician

Health Statisticians collect, analyze, interpret, and disseminate health data to inform public health policy and practice. Population Health: Predictive Analytics provides a foundation for Health Statisticians by introducing key concepts in modeling and statistical analysis, such as study design, missing data handling, and model selection. The course's emphasis on evaluating model validity and updating models for specific settings is particularly relevant for Health Statisticians, enabling them to ensure the accuracy and reliability of their statistical findings.

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Research Scientist

Research Scientists conduct scientific research to advance knowledge in a specific field. This course can be beneficial for Research Scientists working in healthcare or related fields. It provides a comprehensive overview of predictive analytics, including study design, model development, and model validation. The emphasis on evaluating model validity and updating models is particularly relevant for Research Scientists, as they need to ensure the accuracy and reliability of their research findings.

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Risk Analyst

Risk Analysts assess and manage risks in various industries, including healthcare. Population Health: Predictive Analytics provides a solid foundation for Risk Analysts by introducing key concepts in predictive modeling and statistical analysis. The course's emphasis on evaluating model validity and updating models is particularly relevant for Risk Analysts, as they need to ensure the accuracy and reliability of their risk assessments.

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Healthcare Consultant

Healthcare Consultants provide guidance and expertise to healthcare organizations on improving their operations and patient care. The skills acquired in Population Health: Predictive Analytics can be valuable for Healthcare Consultants. The course provides insights into predictive analytics, enabling them to better understand and apply data-driven approaches to healthcare problem-solving. It also covers topics like model selection and evaluating validity, which are essential for ensuring the reliability and effectiveness of predictive models used in healthcare.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical modeling to analyze financial data and make investment decisions. Population Health: Predictive Analytics provides a solid foundation for Quantitative Analysts by introducing key concepts in statistical modeling and predictive analytics. The course's emphasis on model validation and evaluating model performance is particularly relevant for Quantitative Analysts, who need to ensure the reliability and accuracy of their models.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models and systems. This course lays a solid foundation for aspiring Machine Learning Engineers. It covers the fundamental concepts of predictive modeling, including model development, model validation, and model updating. The emphasis on evaluating model validity and handling missing values is particularly relevant for Machine Learning Engineers, as they need to ensure the accuracy and robustness of their models.

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Actuary

Actuaries use mathematical and statistical techniques to assess and manage financial risks. This course can be beneficial for Actuaries working in the healthcare industry. It provides an overview of predictive analytics, including model development, model validation, and model updating. The emphasis on evaluating model validity is particularly relevant for Actuaries, who need to ensure the accuracy and reliability of their risk assessments.

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Epidemiologist

Epidemiologists investigate the causes and distribution of diseases and other health problems in populations. This course may be useful for Epidemiologists seeking to enhance their skills in predictive analytics. It covers key concepts in modeling and statistical analysis, such as study design, missing data handling, and model selection. The emphasis on evaluating model validity is particularly relevant for Epidemiologists, as they need to ensure the accuracy and reliability of their research findings.

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Data Analyst

Data Analysts collect, clean, and analyze data to extract meaningful insights. This course may be useful for Data Analysts seeking to advance their skills in predictive analytics. It covers key concepts in modeling and statistical analysis, such as study design, missing data handling, and model selection. The emphasis on evaluating model validity is particularly relevant for Data Analysts, as they need to ensure the accuracy and reliability of their data-driven insights.

See salaries and explore the career path for Data Analyst

Market Research Analyst

Market Research Analysts collect and analyze data to understand consumer behavior and market trends. This course may be useful for Market Research Analysts seeking to enhance their skills in predictive analytics. It covers key concepts in modeling and statistical analysis, such as study design, missing data handling, and model selection. The emphasis on evaluating model validity is particularly relevant for Market Research Analysts, as they need to ensure the accuracy and reliability of their market insights.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers seeking to enhance their skills in data analysis and modeling. It covers key concepts in modeling and statistical analysis, such as study design, missing data handling, and model selection. The hands-on experience in using R for predictive modeling further enhances the relevance of this course for Software Engineers.

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