Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data
Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data
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使用真实世界数据的公平算法量化侵袭性耐甲氧西林金黄色葡萄球菌感染的健康结果差异
DOI：
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发表时间：
2023
影响因子：
--
通讯作者：
M. Prosperi
中科院分区：
文献类型：
--
作者：
Inyoung Jun;Sara Ser;Scott A. Cohen;Jie Xu;Robert J. Lucero;Jiang Bian;M. Prosperi
本研究通过利用一种新的人工智能（AI）公平算法，即公平感知因果路径（FACTS）分解，并将其应用于现实世界的电子健康记录（EHR）数据，量化了侵袭性耐甲氧西林金黄色葡萄球菌（MRSA）感染的健康结果差异。我们将美国佛罗里达州一家大型医疗保健提供商9年的电子病历与健康的背景社会决定因素（SDoH）在时空上联系起来。我们首先创建了一个因果结构图，将SDoH与侵袭性MRSA感染诊断前/后的个体临床测量、治疗、副作用和结果联系起来；然后，我们应用FACTS量化不同因果途径（包括SDoH、临床和人口变量）的潜在结果差异。我们发现人口统计学和SDoH方面存在适度差异，所有导致年龄、性别、种族和收入差异的主要途径包括合并症。先前的肾脏损害、万古霉素的使用和时间与种族差异有关，而收入、农村地区和可用的医疗设施与性别差异有关。从干预的角度来看，我们的结果强调了制定同时考虑临床因素和SDoH的政策的必要性。总之，这项工作证明了公平人工智能方法在公共卫生环境中的实际效用。
This study quantifies health outcome disparities in invasive Methicillin-Resistant Staphylococcus aureus (MRSA) infections by leveraging a novel artificial intelligence (AI) fairness algorithm, the Fairness-Aware Causal paThs (FACTS) decomposition, and applying it to real-world electronic health record (EHR) data. We spatiotemporally linked 9 years of EHRs from a large healthcare provider in Florida, USA, with contextual social determinants of health (SDoH). We first created a causal structure graph connecting SDoH with individual clinical measurements before/upon diagnosis of invasive MRSA infection, treatments, side effects, and outcomes; then, we applied FACTS to quantify outcome potential disparities of different causal pathways including SDoH, clinical and demographic variables. We found moderate disparity with respect to demographics and SDoH, and all the top ranked pathways that led to outcome disparities in age, gender, race, and income, included comorbidity. Prior kidney impairment, vancomycin use, and timing were associated with racial disparity, while income, rurality, and available healthcare facilities contributed to gender disparity. From an intervention standpoint, our results highlight the necessity of devising policies that consider both clinical factors and SDoH. In conclusion, this work demonstrates a practical utility of fairness AI methods in public health settings.
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影响因子：
120.7
作者：
Cheng, Yiling J.;Kanaya, Alka M.;Imperatore, Giuseppina
通讯作者：
Imperatore, Giuseppina
利用因果生存森林和全州电子健康记录数据的 G 公式，优化针对侵袭性耐甲氧西林金黄色葡萄球菌感染的动态抗生素治疗策略。
DOI：
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发表时间：
2023
期刊：
Proceedings of machine learning research
影响因子：
--
作者：
Jun,Inyoung;Cohen,ScottA;Ser,SarahE;Marini,Simone;Lucero,RobertJ;Bian,Jiang;Prosperi,Mattia
通讯作者：
Prosperi,Mattia
PC-Fairness：衡量基于因果关系的公平性的统一框架
DOI：
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发表时间：
2019
期刊：
NeurIPS 2019
影响因子：
--
作者：
Wu, Yongkai;Zhang, Lu;Wu, Xintao;Tong, Hanghang
通讯作者：
Tong, Hanghang
影响因子：
37.8
作者：
Mosca L;Barrett-Connor E;Wenger NK
通讯作者：
Wenger NK