在医疗补助行政索赔中识别艾滋病毒感染者的竞争算法的有效性:全州分析
April D Kimmel1,2, Zhongzhe Pan1, Kathy K Byrd3
1Department of Health Policy, School of Public Health.
AIDS (London, England)
|October 9, 2025
概括
在行政索赔数据中识别艾滋病毒感染者是一项挑战. 这项研究验证了算法,发现使用多个代码类型的定制方法在索赔数据中为艾滋病毒病例识别提供了良好的性能.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 公共卫生 公共卫生
背景情况:
- 行政索赔数据对艾滋病毒研究和计划实施有价值.
- 在索赔数据中准确识别艾滋病毒 (人类免疫缺陷病毒) 感染者是一个重大挑战.
研究的目的:
- 评估各种病例识别算法的有效性,以在行政索赔数据中检测艾滋病毒感染的成年人.
- 基于不同组合的诊断,程序和处方代码进行算法的性能比较.
主要方法:
- 开发了12个病例识别算法,使用来自弗吉尼亚医疗补助申请 (2012-2023) 的诊断,程序和处方药代码.
- 将索赔数据与来自弗吉尼亚州卫生部的黄金标准艾滋病毒监测数据进行了匹配.
- 计算的诊断准确度指标,包括每个算法的灵敏度,特异性,ROC-AUC,PPV和NPV.
主要成果:
- 单代码算法显示了73-75%的灵敏度和47-70%的正预测值 (PPV).
- 使用多个与艾滋病毒相关的代码的算法显示敏感性降低 (51-67%),但PPV增加 (83-90%).
- 所有算法都实现了高特异性和负预测值 (>99%).
结论:
- 在使用多个代码类型时,病例识别算法在基于人口的索赔数据中表现良好,艾滋病毒流行率低.
- 区分和预测能力之间的权衡需要将算法定制为特定的用例.
- 优化算法选择对于在行政数据中准确识别艾滋病毒病例至关重要.
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