May 7, 2023
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Introduction to Machine Learning Methods
Sunday, 7 May 2023 | Course runs 1 Day
TRACK: Methodological & Statistical Research
LENGTH: 4 Hours | Course runs 1 day
This short course is offered in-person at the ISPOR 2023 conference. Separate registration is required. Visit the ISPOR 2023 website to register and learn more.
8:00AM-12:00PM Eastern Daylight Time (EDT)
Healthcare data are often available to payers and healthcare systems in real time, but are massive, high dimensional, and complex. Artificial intelligence and machine learning merge statistics, computer science, and information theory and offer powerful computational tools to enhance the extraction of useful information from complex healthcare data and prediction accuracy. This course gives an overview of basic machine learning concepts and introduces a few commonly used machine learning techniques and their practical applications in healthcare and pharmaceutical outcomes research. Participants are introduced to foundational principles and concepts of statistical machine learning, then provided with several specific machine learning techniques and their applications in health and pharmaceutical outcomes research. The course faculty use R or Radiant to demonstrate several machine learning methods such as penalized regression and tree-based methods, as well as techniques for dimension reduction/feature selection. Participants gain hands-on practical experiences with machine learning and gain experience interpreting and evaluating the results and prediction performance that comes from machine learning modeling. Distinguishing prediction modeling from causal inference research in pharmacoepidemiology will be also presented and discussed. This is an entry-level course but is designed for those with some familiarity with traditional statistical modeling techniques (eg, linear regression, logistic regression).
PREREQUISITES: To get the most out of the course, students should have a basic statistical background. Participants who wish to gain hands-on experience are required to bring their laptops with Radiant (https://radiant-rstats.github.io/docs/install.html) installed.
Registrants receive a digital course book. Copyright, Trademark and Confidentiality Policies apply.
4 Hours | Course runs 1 Day