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Predictive Analytics for Healthcare

Predictive Analytics for Healthcare

This course introduces the basic predictive modeling paradigm: classification and prediction, with applications in healthcare.

Overview

In this course you will be introduced to basic concepts in predictive analytics, also called predictive modeling, with applications to healthcare. You will cover two core paradigms that account for most business applications of predictive modeling: classification, and prediction of numerical quantities. You will review case studies in healthcare applications, and work on practical exercises to predict individuals’ consumption of healthcare services.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

In this course you will learn how to:

 

 

  • Organize the predictive modeling task and data flow
  • Develop machine learning models with linear and logistic regression, and with decision tree and ensemble algorithms using R
  • Assess the performance of these models with holdout data
  • Apply predictive models to generate predictions for new data
  • Use various R packages to implement the models in the course

Who Should Take This Course

Healthcare providers (both administrative and medical), healthcare insurers and government health agencies.

Course Syllabus

Week 1

Preparation

  • Key considerations of data mining in the healthcare context
  • What is supervised learning
  • Data partitioning and holdout samples
  • Choosing variables (features)
  • Handling missing data
  • Assessing models
    • Confusion matrix
    • Misclassification costs
    • Lift

Week 2

Linear and Logistic Regression

  • Fitting a linear regression model
  • Fitting a logistic regression model
  • Assessing model performance
  • Case study – opioid use prediction

 

Week 3

Decision Trees and Ensembles

 

  • Full Bayes classifier
  • Naive Bayes classifier
  • Classification and Regression Trees (CART)
    • Growing the tree
    • Avoiding overfit – pruning
    • Using trees for classifications and predictions
  • Ensembles
    • Random forests
    • Boosted trees

Week 4

Bias and Unfairness

  • Why it happens
  • The healthcare context
  • The role of “black box” models
  • Interpretability methods can help
  • Auditing for fairness

Class Dates

2024

01/19/2024 to 02/16/2024
Instructors: Dr. Margrét Vilborg Bjarnadóttir
08/23/2024 to 09/20/2024
Instructors: Dr. Margrét Vilborg Bjarnadóttir

2025

01/17/2025 to 02/14/2025
Instructors: Dr. Margrét Vilborg Bjarnadóttir
08/22/2025 to 09/12/2025
Instructors: Dr. Margrét Vilborg Bjarnadóttir

Prerequisites

You should be comfortable with using R to fit models.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Predictive Analytics for Healthcare

Additional Information

Homework

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course data modeling project.

In addition to assigned readings, this course also has supplemental video lectures.

Course Text

The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R,  by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the follow on courses: “Predictive Analytics 2 – Neural Nets and Regression – with R” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with R.”  Additional readings with specific application to healthcare will be provided.

Software

This is a hands-on course, and participants will apply data mining algorithms to real data.  The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

Supplemental Information

Take a 10-question quiz on analytics: Test Yourself

 

Literacy, Accessibility, and Dyslexia

At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:

 

Chrome

 

Firefox

 

Safari

  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

 

 

Register For This Course

Predictive Analytics for Healthcare