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Population Health: Predictive Analytics

Approx. 18 hours to complete

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Course Summary

This course explores the use of population health predictive analytics to improve health outcomes and reduce healthcare costs. Students will learn how to use data to identify health risks and develop interventions to address them.

Key Learning Points

Understand the role of data analytics in population health management
Learn how to use predictive models to identify health risks and develop interventions
Explore case studies of successful population health management programs

Job Positions & Salaries of people who have taken this course might have

- USA: $65,000 - $90,000
- India: ₹6,00,000 - ₹10,00,000
- Spain: €25,000 - €40,000
- USA: $65,000 - $90,000
- India: ₹6,00,000 - ₹10,00,000
- Spain: €25,000 - €40,000
- USA: $50,000 - $75,000
- India: ₹3,50,000 - ₹8,00,000
- Spain: €20,000 - €35,000
- USA: $65,000 - $90,000
- India: ₹6,00,000 - ₹10,00,000
- Spain: €25,000 - €40,000
- USA: $50,000 - $75,000
- India: ₹3,50,000 - ₹8,00,000
- Spain: €20,000 - €35,000
- USA: $90,000 - $120,000
- India: ₹12,00,000 - ₹20,00,000
- Spain: €40,000 - €55,000

Learning Outcomes

Understand the principles of population health management
Learn how to use data to identify health risks and develop interventions
Develop skills in predictive modeling and data analysis

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with healthcare data and terminology

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online
Video lectures
Case studies

Similar Courses

Healthcare Analytics: From Data to Insights
Data Science for Healthcare

Related Education Paths

Notable People in This Field

Cardiologist and Digital Medicine Researcher
Surgeon and Public Health Researcher

Related Books

Description

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).

Knowledge

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

Outline

Welcome to Leiden University
Welcome to the course Predictive Analytics
How to succeed in your online class?
Meet the instructors & the team
Leiden University: Facts & Figures
About this course
Glossary
Community Guidelines
What is your learning path?

Prediction for prevention, diagnosis, and effectiveness
Introduction
Introduction to predictive analytics
Predictive analytics in prevention
Predictive analytics diagnosis
Predictive analytics in intervention
To conclude
Introductory assignment
Prevention assignment
Diagnosis assignment
Intervention assignment
Reflect on your goals
Test your knowledge

Modeling Concepts
Introduction
Design issues
Sample size
Overfitting
Bootstrapping
To conclude
Is caring about measurement error an error?
Sample size
Bootstrapping 101 in R
Testimation bias - an interactive introduction
Reflect on your goals
Test your knowledge

Model development
Introduction
Missing values
Continuous predictors
Model selection
Model estimation
To conclude
Bias, precision and simple imputation of missing values
Dealing with non-linearity
Model selection
Model estimation
Reflect on your goals

Model validation and updating
Introduction
Performance measures
Validation approaches
Updating approaches
Predictive analytics for Aruba
To conclude
Performance I - Statistical measures
Performance II - Evaluation of usefulness
Recall - Performance I
Validation cardiovascular disease
Reflect on your goals
Test your knowledge
Final Assessment

Summary of User Reviews

Learn about population health predictive analytics in this comprehensive course on Coursera. This course has received positive reviews from users and is highly recommended. Many users have appreciated the in-depth coverage of the subject matter.

Key Aspect Users Liked About This Course

In-depth coverage of the subject matter

Pros from User Reviews

Comprehensive course
Expert instructors
Interactive content
Clear explanations
Real-world applications

Cons from User Reviews

Heavy on technical jargon
Requires prior knowledge of statistics
Not suitable for beginners
Limited interaction with instructors
Lengthy lectures

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