October 17, 2024
12:15 pm – 1:15 pm

Speaker:
Aaron Mullen, Institute for Biomedical Informatics, University of Kentucky

Where:
VIRTUAL!
(Zoom link: https://uky.zoom.us/j/82438134047)

Title:
Forecasting Opioid Incidents for Rapid Actionable Data for Opioid Response in Kentucky

Abstract:
We present efforts in the fields of machine learning and time series forecasting to accurately predict counts of future opioid overdose incidents around the state of Kentucky. If successful, state governments could use forecasts to properly prepare and distribute resources effectively. The approach taken primarily uses county and district level aggregations of EMS opioid overdose encounters and forecasts future counts at a monthly level. A variety of additional covariates were also tested to determine their impact on the model’s performance. Models with different levels of complexity were evaluated as well to optimize training time and accuracy. The results of this exploration show that when special precautions are taken to address data sparsity, useful predictions can be generated with limited error by utilizing yearly trends and covariance with additional data sources.


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