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Tuesday, September 24
Tue, Sep 24, 4:15 PM - 5:30 PM
Recent Applications and Developments in Machine Learning

Generating Real-World Evidence for Prescription Opioid Use with Geographically Referenced Data Enrichment and Machine Learning (301014)

Grace Chai, FDA 
Xiaoqing Huang, FDA 
Yong Ma, FDA/CDER 
Shekhar Mehta, FDA 
Rose Radin, FDA 
Travis Ready, FDA 
*Jaejoon Song, FDA 
Saranrat Wittayanukorn, FDA 
Corinne Woods, FDA 
Yueqin Zhao, FDA 

Keywords: real-world evidence, prescription opioid use, pharmacovigilance, data enrichment, machine learning

The crisis of opioid abuse and overdose in the United States has involved unprecedented levels of opioid prescriptions and opioid-related mortality. Greater understanding of current trends in prescription opioid utilization may help prevent new cases of abuse, addiction, and overdose.

The U.S. Food and Drug Administration (FDA, the Agency) is expanding its capacity for proactive pharmacovigilance of drug abuse, in addition to other drug safety signals. In post-market safety surveillance, pharmacy dispensing data provide valuable insights to the Agency for oversight of drug utilization. The drug dispensing data include the number of product dispensings aggregated over a time frame (e.g., months) by geographical locations (e.g., states, core-based statistical areas). One promising approach to enhance pharmacovigilance using these data would be through data enrichment: geographically referenced public data sources covering detailed information on demographic, socioeconomic, and healthcare service can be overlaid to proprietary, nationally projected data for prescription drug dispensing.

Our project, funded by the Center for Drug Evaluation and Research (CDER) Safety Research Interest Group (SRIG) program, seeks to develop a data analysis pipeline and software for generating real-world evidence (RWE) that will monitor changes in prescription opioid use and guide proactive pharmacovigilance of drug abuse. The software will provide tools to augment proprietary, nationally projected data for prescription drug dispensing with other geographically referenced, publicly available, demographic, socioeconomic, or healthcare service data. The software will generate RWE including user-interactive data visualization, spatio-temporal modeling, and machine learning for identifying factors potentially associated with drug utilization, misuse, and abuse.