在电子健康记录系统中构建艾滋病毒风险预测模型的通用管道
Sarah B May1,2, Thomas P Giordano3,4, Assaf Gottlieb1
1Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, United States.
Journal of the American Medical Informatics Association : JAMIA
|November 22, 2023
概括
新的自动化模型可以准确地预测各种健康记录中的艾滋病毒风险,改善了包括女性在内的高风险个体的识别. 这有助于及时测试和预防战略,以应对当前的艾滋病毒流行.
科学领域:
- 公共卫生 公共卫生
- 医疗保健中的机器学习
- 流行病学 流行病学
背景情况:
- 人类免疫缺陷病毒 (HIV) 流行仍然是美国的主要公共卫生问题.
- 有效的艾滋病毒风险预测模型对于通过识别有风险的个体进行检测和干预,如暴露前预防 (PrEP) 等干预来减少传播至关重要.
- 当前的艾滋病毒风险算法通常需要手动的特征工程,并且在不同的电子健康记录 (EHR) 系统中难以泛化,在预测女性风险时的准确性有限.
研究的目的:
- 开发和评估一个自动化的管道,用于构建HIV风险预测模型,使用自动功能工程.
- 评估这些模型在各种电子健康记录系统和数据类型中的性能.
- 将一般艾滋病毒风险模型的有效性与女性特异性模型进行比较,特别是用于预测女性的艾滋病毒风险.
主要方法:
- 为自动功能工程开发了一个管道,以创建HIV风险的预测模型.
- 该管道在当地EHR系统和国家索赔数据上进行了测试.
- 通过将一般模型与女性特有的模型进行比较来评估性能.
主要成果:
- 自动化模型在不同的健康记录数据集中显示出强大的性能,实现曲线下的面积 (AUC) 为0.87.
- 一般模型在预测女性艾滋病毒风险方面表现良好.
- 女性特异型模型进一步提高了女性的预测准确性,AUC从0.81到0.86.
结论:
- 灵活的,自动化的预测模型构建是有效的艾滋病毒风险预测在不同的医疗保健系统.
- 这些模型在预测女性艾滋病毒风险方面表现相对较好,解决了先前的局限性.
- 证明的性能和适应性使得这些自动化HIV风险预测模型在现有的医疗保健基础设施中可行.
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