Predictive Analytics for Healthcare

Predictive Analytics for Healthcare

This course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.

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

$899 | Enroll Now
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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 Level
4-Week Course
100% Online Courses
ACE + CAP Credit Eligible
Expert Instructors
Teacher Assistant Support
Tution-Back Guarantee

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.

Instructors

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Margret Bjarnadottir

Dr. Margrét Vilborg Bjarnadóttir

Dr. Margrét Vilborg Bjarnadóttir, Associate Professor of Management Science and Statistics at the University of Maryland and Founder and Chairwoman of PayAnalytics, specializes in operations research methods using large scale data; her research centers around data driven decision making, combining optimization modeling with data analytics.  In health-care Dr. Bjarnadóttir specializes in decision modeling using EMR and claims data. Her recent papers include: “Aiding the Prescriber” which focuses on risk modeling for improved opioid prescriptions and “Predicting Colorectal Cancer Mortality” which utilizes EMR and Cancer Registry data to ...

See Instructor Bio

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

2023

Aug 25, 2023 to Sep 22, 2023

2024

Jan 19, 2024 to Feb 16, 2024

Aug 23, 2024 to Sep 20, 2024

2025

No classes scheduled at this time.

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Prerequisites

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

You should be comfortable with using R to fit models.

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What Our Students Say​

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Marta Fedyna

The book for this course is very good. Everything is explained in a really clear way. Videos were great too and help in understanding issues.

Marta Fedyna
Data Engineer w HSBC
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Jeff Mantel

Anthony did a great job of answering questions, adding explanations and expanding on ideas. The best teacher I have had at statistics.com so far

Jeff Mantel
Mantel Consulting
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Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

Please order a copy of your course textbook prior to course start date.

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.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for college credit.  For recommendation details (level, and number of credits), please see this page. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

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)

 

 

Miscellaneous

There is no additional information for this course.

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Predictive Analytics for Healthcare
$899 | Enroll Now
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