Big data and predictive modelling for the opioid crisis: existing research and future potential

Area of Science:

• Public Health
• Data Science
• Pharmacology

Background:

• Opioid use disorder (OUD) presents a significant public health challenge, necessitating improved overdose risk estimation and intervention strategies.
• Global trends indicate rising opioid use and overdose deaths, highlighting the urgent need for effective harm reduction approaches.

Purpose of the Study:

• To explore the potential of predictive analytics and big data for reducing overdose risk in individuals with OUD.
• To summarize global trends in opioid use and overdoses and discuss the application of big data in overdose research.
• To examine the role of predictive modeling, including machine learning, in preventing and monitoring opioid overdoses.

Main Methods:

• Review of global trends in opioid use and overdose data.
• Analysis of big data research methodologies applied to opioid overdose.
• Exploration of predictive modeling techniques, including machine learning, for overdose prevention and monitoring.

Main Results:

• Big data and predictive modeling offer promising avenues for understanding and mitigating opioid overdose risks.
• Machine learning can enhance the monitoring and prevention of opioid-related harms.
• Challenges and risks associated with big data and machine learning in this field require careful consideration.

Conclusions:

• Collaborative efforts across agencies are crucial for advancing research and improving interventions for OUD.
• Predictive modeling can facilitate a stratified medicine approach in public health for OUD, enabling personalized diagnoses, prognoses, and treatment recommendations.