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机器学习在瑞士与男性发生性关系的男性中识别了性行为子组
Luisa Salazar-Vizcaya1, Dunja Nicca2, Vanessa Christinet3
1Department of Infectious Diseases, Inselspital Bern University Hospital, University of Bern, Anna-Seiler-Haus, Geschoss J, 3010, Bern, Switzerland. luisapaola.salazarvizcaya@insel.ch.
Archives of sexual behavior
|July 7, 2025
概括
研究人员使用机器学习在与男性发生性关系的男性 (MSM) 中确定了不同的性行为子组. 首次访问数据有效预测了子组成员资格,使得量身定制的性健康策略成为可能.
科学领域:
- 公共卫生 公共卫生
- 计算流行病学计算流行病学
- 行为科学 行为科学
背景情况:
- 性行为是复杂的,并且在个体之间有很大的差异.
- 了解这些不同的模式对于开发有效的公共卫生干预措施和性健康信息至关重要.
- 与男性发生性关系的男性 (MSM) 是针对性性健康倡议的关键人口.
研究的目的:
- 开发和验证一种机器学习方法,用于在MSM人口中表征不同的性行为子组.
- 评估初次访问数据的预测能力,以识别这些子组.
- 为特定子组提供定制性健康信息的开发信息.
主要方法:
- 基于纵向性行为数据,使用等级分类对2349名HIV阴性MSM进行了分组.
- 采用随机森林分类模型,利用第一次访问的数据预测子组成员.
- 从2016年11月至2019年4月期间,从瑞士的性健康咨询中心收集了数据.
主要成果:
- 确定了六个具有独特性行为趋势的不同子组,这些子组与整体人口趋势有很大差异.
- 两个子组 (37%的参与者) 与不稳定的伴侣,群体性和大量伴侣的无避孕套交增加70%以上有关.
- 第一次访问数据准确地预测了子组成员资格,预测准确度在64%至86%之间.
结论:
- 该研究成功地确定了MSM中特定的性行为子组,突出了这一群体内的异质性.
- 最初的访问数据可以可靠地预测子组成员资格,为早期识别提供了一种实用的方法.
- 该方法提供了一个算法工具,用于创建有针对性和个性化的性健康信息,改善公共卫生宣传和性健康结果.
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