使用机器学习估计跨交叉社会地位的物质使用差异:群体-拉索交互网络应用
Connor J McCabe1, Jonathan L Helm2, Max A Halvorson1
1Department of Psychiatry, University of Washington.
概括
一种新的机器学习方法,群体激光交互网络 (glinternet),提供了比传统回归更稳定的社会地位健康差异估计. 这种方法有助于更好地了解不同人群中物质使用的流行率.
科学领域:
- 量化交叉研究的交叉研究.
- 健康差异研究 研究健康差异研究
- 机器学习在卫生中的应用.
背景情况:
- 传统的回归方法在交叉研究中难以处理稀疏的数据和高维度.
- 机器学习为分析复杂交互提供了灵活的替代方案.
- 交叉方法对于理解跨多个社会地位的健康风险行为至关重要.
研究的目的:
- 引入和评估组-拉索交互网络 (glinternet) 以评估物质使用的交叉差异.
- 为了将glinternet的性能与健康差异建模的传统物流回归进行比较.
- 利用机器学习在定量交叉研究中提供更可靠的效果估计.
主要方法:
- 使用带有层次规范化的glinternet来分析性别,性取向和种族之间的双向相互作用.
- 利用来自我们所有人研究计划的大型国家数据集 (N = 283,403).
- 使用持久交叉验证验证验证结果,并将估计值与后勤回归进行比较.
主要成果:
- 格林网提供了比后勤回归更稳定的流行率估计,特别是在代表性不足的群体.
- 在性少数群体和白人 cisgender 女性中发现烟草和大麻使用率较高.
- 通过使用glinternet.net,证明了改进的模型节性和参数稳定性.
结论:
- 格林互联网提供了一个有前途的替代方案,以调节多重回归的交叉健康研究.
- 该方法提高了跨越交叉的社会地位的健康差异的量化.
- 在复杂的健康行为分析中,Glinternet的稳定性和灵活性可以提高效果估计的可靠性.
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