使用公开可用的数据来预测县级的娱乐大麻合法化:一种机器学习方法
Barrett Wallace Montgomery1, Xiaoran Tong2, Olga Vsevolozhskaya2
1Research Triangle Institute, International, Research Triangle Park, NC, United States.
The International journal on drug policy
|February 11, 2024
概括
机器学习使用人口和物质使用数据准确地预测了美国各县的娱乐大麻合法性. 上个月大麻和过去一年可卡因使用率较高是合法销售的关键预测因素.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 计算社会科学 计算社会科学
背景情况:
- 在已经将娱乐大麻合法化的州内,当地大麻政策存在显著的地理差异.
- 预测当地大麻政策的采用对于了解公共卫生和经济影响至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,以预测美国县级的娱乐大麻销售合法性.
- 确定影响当地大麻政策决策的关键人口,社会政治和物质使用因素.
主要方法:
- 合并了来自2010年人口普查,2012年总统选举数据和国家药物使用和健康调查 (NSDUH) (2010-2012) 的县级数据.
- 开发了一组1000个逻辑回归,使用合成数据增强和少数超标采样.
- 通过预测2014年娱乐大麻合法化 (RCL) 政策 (92个RCL,3002个非RCL县) 评估模型性能.
主要成果:
- 整体模型在预测过渡到RCL的县里获得了0.61的宏观f1平均得分.
- 过去一个月使用大麻和过去一年使用可卡因的流行率是合法娱乐大麻销售的最强预测因素.
- 该模型在预测县级政策变化时显示出明显的歧视.
结论:
- 从2010-2012年公开可用的数据可以被利用来预测娱乐大麻销售合法性,准确度适度.
- 该模型的表现突显了物质使用模式对当地大麻政策制定的影响.
- 建议使用最新数据和先进模型 (例如,时间到事件) 进行进一步的研究,以提高预测准确度.
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