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Development of a Machine Learning Model to Estimate US Firearm Homicides in Near Real Time

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Key Points Question Can diverse secondary data sources accurately estimate US firearm homicides in near real time? Findings This national prognostic study combines data from 5 online, health service, and… Click to show full abstract

Key Points Question Can diverse secondary data sources accurately estimate US firearm homicides in near real time? Findings This national prognostic study combines data from 5 online, health service, and hotline data sources into an ensemble model that accurately (99.74%) forecasted firearm homicide deaths in near real time within 38 deaths for the year. Meaning The findings of this study suggest that this model for forecasting firearm homicides provides a viable process to facilitate timely prevention efforts and expand firearm violence research using secondary data sources.

Keywords: estimate firearm; near real; real time; model; firearm homicides

Journal Title: JAMA Network Open
Year Published: 2023

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