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使用机器学习和预测建模方法,比较与与艾滋病毒状况相关的兴奋剂使用增加相关的因素
Cheríe S Blair1, Marjan Javanbakht2, W Scott Comulada3,4
1Department of Medicine, Division of Infectious Diseases, David Geffen School of Medicine at UCLA, 10833 Le Conte Avenue, CHS 52-215, Los Angeles, CA, 90095, USA. cherieblair@mednet.ucla.edu.
概括
兴奋剂的使用增加了与男性发生性关系 (MSM) 的男性的HIV/STI风险. 诸如不稳定的住房和交易性等因素与增加的兴奋剂使用有关,因艾滋病毒状况而异.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 机器学习在健康中的应用
背景情况:
- 兴奋剂的使用是艾滋病毒和性传播感染 (STI) 在与男性发生性关系的男性 (MSM) 之间传播的重要因素.
- 了解与增加兴奋剂使用相关的特征对于为这一群体制定有效的艾滋病毒预防策略至关重要.
研究的目的:
- 采用机器学习技术来识别与MSM中兴奋剂使用增加相关的因素.
- 调查这些影响因素是否根据艾滋病毒状况 (阳性或阴性) 变化.
主要方法:
- 利用了来自加利福尼亚州洛杉矶主要为黑人/拉丁裔MSM的纵向队列的数据,从2014年8月到2020年12月.
- 采用最小绝对收缩和选择运算符 (LASSO) 进行变量选择和预测模型创建以增加兴奋剂使用.
- 使用混合效应逻辑回归,按艾滋病毒状态分层,以分析选定变量与增加兴奋剂使用之间的关联.
主要成果:
- 在20.9%的研究访问中,观察到使用兴奋剂的增加.
- 总的来说,增加兴奋剂的使用与不稳定的住房,性传播疾病的诊断,交易性行为和伴侣使用兴奋剂有关.
- 在艾滋病毒感染的MSM中,增加的兴奋剂使用与过度饮酒,电子烟/香烟使用和泡使用相关. 在HIV阴性MSM中,相关性包括醉酒时的群体性行为,交易性行为和伴侣注射药物使用.
结论:
- 在这种情况下,机器学习,特别是LASSO,对于变量选择和预测建模是有效的.
- 该研究强调,与增加兴奋剂使用相关的风险行为在HIV阳性和HIV阴性MSM之间存在显著差异.
- 调查结果强调,在为MSM设计有针对性的艾滋病毒预防和治疗干预措施时,需要考虑共用物质和伙伴关系动态.
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