实验室测试可用于预测艾滋病毒感染者中酸乙醇测量的高风险酒精使用:使用机器学习的概念验证
C Espinosa da Silva1, A Scheffler2, R Fatch1
1Department of Medicine, University of California San Francisco, 550 16th Street, San Francisco, CA, 94158, USA.
Alcohol and alcoholism (Oxford, Oxfordshire)
|December 15, 2025
概括
使用实验室和健康数据的机器学习模型可以预测艾滋病毒感染者 (PWH) 的高风险饮酒情况. 这种方法为昂贵的直接措施提供了有价值的替代方案,例如酸乙醇 (PEth) 测试.
科学领域:
- 生物医学研究的研究.
- 在医疗保健中的数据科学.
- 公共卫生 公共卫生
背景情况:
- 不健康的酒精使用在艾滋病毒感染者中很常见,导致更糟糕的健康结果.
- 自我报告的酒精使用往往被低估.
- 酸乙醇 (PEth) 直接测量酒精消耗,但价格昂贵.
研究的目的:
- 为了评估机器学习是否可以预测高风险的酒精使用 (以PEth测量) 在PWH使用现有的实验室和健康数据.
- 确定与高风险饮酒相关的间接生理标志物.
主要方法:
- 从乌干达的988个PWH中汇总了基线数据.
- 被归类为高风险的酒精使用,PEth ≥200 ng/ml.
- 利用LASSO逻辑回归,梯度提升树木和随机森林与29个实验室和健康预测器.
- 将数据分为训练 (n=790) 和测试 (n=198) 集.
主要成果:
- 使用17个预测因素的Lasso回归模型实现了最佳性能.
- 在最佳模型中,在训练组中,交叉验证的AUC为0.751 (95% CI:0.718-0.784).
- 该模型在测试组中实现了0.795的AUC (95%CI:0.723-0.852).
结论:
- 实验室和健康数据可以有效地识别高风险酒精使用的PWH.
- 结合间接酒精标记器的机器学习算法显示出对临床和研究环境的希望.
- 这种方法可能特别有用,当直接的PEth测试是不可行的.
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