Summary of Forecasting Opioid Incidents For Rapid Actionable Data For Opioid Response in Kentucky, by Aaron D. Mullen et al.
Forecasting Opioid Incidents for Rapid Actionable Data for Opioid Response in Kentucky
by Aaron D. Mullen, Daniel Harris, Peter Rock, Svetla Slavova, Jeffery Talbert, V.K. Cody Bumgardner
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a machine learning approach for forecasting future opioid overdose incidents in Kentucky, aiming to help state government agencies prepare resources effectively. The method uses county-level aggregations of EMS encounters and forecasts monthly counts, considering various covariates’ impact on performance. Models with different complexities are evaluated to optimize training time and accuracy. The results show that by addressing data sparsity and utilizing yearly trends and covariance with additional sources, useful predictions can be generated with limited error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to predict how many opioid overdose incidents will happen in the future in Kentucky. They use information about where these overdoses happened before to make a good guess. This is important because it helps government agencies get ready for what might happen and send the right resources to the right places. The researchers tested different ways of doing this, like using more or less complicated models, and found that if they take care to deal with gaps in their data, they can make pretty accurate predictions. |
Keywords
* Artificial intelligence * Machine learning
