Analysis of Real World Evidence using Machine Learning to Improve Chronic Pain Treatment Outcomes
To Know
Where:
Bldg 40 1201/1203
Organizer:
NIH Pain SIG
Presented By:
Ajay Wasan (University of Pittsburgh)
About this Class
This talk will focus on analyses of the Patient Outcomes Repository for Treatment (PORT) which is a large registry of chronic pain treatment outcomes from patients seen in the pain clinics at the University Pittsburgh Medical Center (UPMC). Using methods such as propensity scoring, stratified modeling, and supervised machine learning, we can determine which treatments for chronic pain are or are not effective, the phenotypes most responsive to each treatment, and predict which treatments will be most effective in any new patient based on their phenotype (such as medications, injections, physical therapy, or mental health care).