通过针对性的艾滋病毒查,积极地在社区中识别和早期检测艾滋病毒.
Mehdi Nejat1, Hamid Reza Marateb2,3, Mehrshad Alirezaei Farahani2
1Department of Biostatistics and Epidemiology, School of Health, and Student Research Committee, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Scientific reports
|July 27, 2025
概括
机器学习模型可以使用人口统计和生活方式因素预测人类免疫缺陷病毒 (HIV) 风险. 这种方法有助于在资源有限的环境中早期发现和干预.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗保健中的机器学习
背景情况:
- 人类免疫缺陷病毒 (HIV) 构成了重大的全球健康挑战,特别是在资源有限的环境中.
- 早期艾滋病毒检测对于有效的干预至关重要,但经常受到耻辱和有限的检测机会的阻碍.
- 预测模型可以通过识别更高风险的个体来增强艾滋病毒查.
研究的目的:
- 开发和验证机器学习模型,使用人口和生活方式变量预测艾滋病毒风险.
- 改善在资源有限的环境中早期发现艾滋病毒.
- 为艾滋病毒风险评估创建一个公开可访问的工具.
主要方法:
- 分析了来自伊朗希拉兹的39,295个人的数据.
- 开发和验证一个极端梯度增强 (XGBoost) 模型.
- 使用精度,AUC和科恩的卡帕进行分层的五倍交叉验证和性能评估.
主要成果:
- 确定的艾滋病毒风险的关键预测因素包括注射药物,年龄,配偶的艾滋病毒状况,职业和监狱记录.
- XGBoost模型 (PREDICT-HIV) 实现了高精度 (0.89) 和良好的区分能力 (AUC=0.84).
- 模型性能在验证折叠中是一致的,表明了稳定性.
结论:
- 社会人口和行为因素对于HIV风险预测至关重要.
- 普雷迪克特-艾滋病毒模型显示了在资源有限的环境中实际实施早期识别的潜力.
- 在多样化的人口中进一步验证,并纳入社会经济变量,可以加强全球艾滋病毒预防工作.
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