May 5: Introduction to Machine Learning Methods- In Person at ISPOR 2024
event-Short-Courses

May 5, 2024

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Introduction to Machine Learning Methods (in person at ISPOR 2024)

LEVEL: 
Intermediate
TRACK: 
Methodological & Statistical Research
LENGTH:
4 Hours | Course runs 1 day

This short course is offered in-person at the ISPOR 2024 conference. Separate registration is required.  Visit the ISPOR 2024 Program page to register and learn more.

Sunday, 5 May 2024 | Course runs 1 Day
08:00-12:00 Eastern Standard Time (EST)

DESCRIPTION

Separate registration required.

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 will be introduced to foundational principles and concepts of statistical machine learning, then be provided with several specific machine learning techniques and their applications in health and pharmaceutical outcomes research. The course faculty will 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 will have 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.

 

FACULTY MEMBERS

William V. Padula, PhD, MSc, MS
Assistant Professor
University of Southern California
Los Angeles, CA

Wei-Hsuan Jenny Lo-Ciganic, MSPharm, MS, PhD
Professor of Medicine
Division of General Internal Medicine
University of Pittsburgh
Pittsburgh, PA, USA, and
Health Research Scientist
Geriatric Research Education and Clinical Center (GRECC)
North Florida/South Georgia Veterans Health System
Gainesville, FL, USA

 

Basic Schedule:

4 Hours | Course runs 1 Day

ISPOR short courses are designed to enhance knowledge and techniques in core health economics and outcomes research (HEOR) topics as well as emerging trends in the field. Short courses offer 4 or 8 hours of premium scientific education and an electronic course book. Active attendee participation combined with our expert faculty creates an immersive and impactful learning experience. Short courses are not recorded and are only available during the live course presentation.

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