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使用机器学习来识别城市卫生系统中患有艾滋病毒风险的患者

Arun Kumar Nethi¹, Albert George Karam¹, Kristin S Alvarez²

¹PCCI, Dallas, TX.

Journal of acquired immune deficiency syndromes (1999)

|August 8, 2024

概括

机器学习模型可以使用电子健康记录预测艾滋病毒风险,改善患者选择预防性护理. 这种艾滋病毒风险预测工具增强了主动干预的宣传力度.

科学领域:

机器学习在公共卫生中的应用.
用于传染病监测的预测建模.

背景情况:

有效的艾滋病毒预防策略存在,但识别有风险的个体仍然是一个挑战.
通过准确预测艾滋病毒风险,可以加强积极的患者外展.

研究的目的:

开发和评估用于预测人类免疫缺陷病毒 (HIV) 病例的机器学习模型.
通过预测性风险建模,加强患者选择艾滋病毒预防措施.

主要方法:

分析了2015-2019年16岁以上的458,893名患者的队列.
采用轻度梯度增强机 (LGBM) 算法,使用 70:30 数据分割进行训练和验证.
模型性能使用k倍交叉验证和接收器操作特征曲线 (AUC) 下的面积进行评估.

主要成果:

梯度增强分类器在训练组中实现了0.88的AUC,在验证组中达到0.85.
该模型在识别有风险的患者时显示了77.8%的灵敏度和75.1%的特异性.
该LGBM模型显示出强大的预测能力,用于识别适合HIV暴露前预防 (PrEP) 的候选人.

结论:

可以利用电子健康记录数据建立有效的梯度增强模型来预测艾滋病毒风险.
这种预测模型可以在临床环境中实施,以促进针对HIV预防的有针对性的宣传.
机器学习提供了一种有前途的方法,可以主动识别和支持那些有感染艾滋病毒风险的人.

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