普罗维达:开发和验证机器学习模型,以预测罗德岛的社区级过量剂量风险
Bennett Allen1, Robert C Schell2, Victoria A Jent1
1From the Center for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA.
Epidemiology (Cambridge, Mass.)
|January 5, 2024
概括
机器学习准确地预测邻里过量使用风险,指导资源分配. 这种方法有助于公共卫生官员在高风险地区有效地针对干预措施.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 药物过量仍然是一个关键的公共卫生问题在美国,紧张有限的资源.
- 有效地分配稀缺的资源对于预防过量服用的努力至关重要.
- 机器学习 (ML) 提供了一种有前途的策略,用于识别未来过量使用风险较高的领域.
研究的目的:
- 开发和评估一种ML模型,用于预测邻里级致命过量使用风险.
- 评估模型在识别高风险区域的准确性,以便主动分配资源.
- 将这种预测模型嵌入随机试验中,以测量其对全州过量剂量率的影响.
主要方法:
- 利用罗德岛 (RI) 的全州数据,涵盖2016-2020年.
- 开发了一个集体ML模型,结合了梯度增强和超级学习者模型.
- 采用移动窗口框架进行6个月间隔预测,针对前20%的社区,其中40%的过量死亡.
主要成果:
- 该模型成功地确定了优先社区,在测试期间捕获了40.2%的过量死亡,在验证期间捕获了44.1%.
- 总体模型在测试期间表现出高于基准模型的性能.
- 验证期间的性能与表现最好的单个基准模型相美.
结论:
- 机器学习模型可以准确地预测社区级致命过量风险.
- 证明的准确性适用于公共卫生从业人员的实际应用.
- 预测建模可以作为一个有价值的工具来指导有限的公共卫生资源的分配.
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