目标:现有的公平性评估方法往往忽视了健康的社会决定因素的系统性差异,比如人口统计学和社会经济学,在比较组中,可能导致不准确甚至矛盾的结论。这项研究旨在使用考虑系统差异的公平性检测方法来评估预测慢性病患者死亡率的种族差异。
方法:我们从MassGeneralBrigham的电子健康记录(EHR)中创建了五个数据集,每个人都专注于不同的慢性病:充血性心力衰竭(CHF),慢性肾脏病(CKD),慢性阻塞性肺疾病(COPD),慢性肝病(CLD),和痴呆症。对于每个数据集,我们开发了单独的机器学习模型来预测1年死亡率,并通过比较黑人和白人个体的预测表现来检验种族差异.我们比较了总体黑人和白人与他们的同行之间的种族公平性评估,这些人是黑人和匹配的白人,通过倾向得分匹配确定,系统差异得到缓解。
结果:我们发现黑人和白人在年龄上存在显著差异,性别,婚姻状况,教育水平,吸烟状况,健康保险类型,身体质量指数,和Charlson合并症指数(p值<0.001)。当检查通过倾向得分匹配确定的匹配的黑人和白人亚群时,特定协变量之间存在显著差异。我们观察到CHF队列中保险类型的显著性水平较弱(p=0.043),在CKD队列中,保险类型(p=0.005)和教育水平(p=0.016),和痴呆队列中的体重指数(p=0.041);其他协变量没有显着差异。在检查五个研究队列的死亡率预测模型时,我们对消除系统性差异前后的公平性评价进行了比较。我们揭示了CHF队列的显着差异,在AdaBoost模型的F1测量和敏感性方面,p值为0.021和0.001,就MLP模型的F1度量和灵敏度而言,p值为0.014和0.003,分别。
结论:这项研究通过关注系统差异的检查,为公平性评估的研究做出了贡献,并强调了在临床环境中使用的机器学习模型中揭示种族偏见的可能性。
OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory conclusions. This
study aims to evaluate racial disparities in predicting mortality among patients with chronic diseases using a fairness detection method that considers systematic differences.
METHODS: We created five datasets from Mass General Brigham\'s electronic health records (EHR), each focusing on a different chronic condition: congestive heart failure (CHF), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), chronic liver disease (CLD), and dementia. For each dataset, we developed separate machine learning models to predict 1-year mortality and examined racial disparities by comparing prediction performances between Black and White individuals. We compared racial fairness evaluation between the overall Black and White individuals versus their counterparts who were Black and matched White individuals identified by propensity score matching, where the systematic differences were mitigated.
RESULTS: We identified significant differences between Black and White individuals in age, gender, marital status, education level, smoking status, health insurance type, body mass index, and Charlson comorbidity index (p-value < 0.001). When examining matched Black and White subpopulations identified through propensity score matching, significant differences between particular covariates existed. We observed weaker significance levels in the CHF cohort for insurance type (p = 0.043), in the CKD cohort for insurance type (p = 0.005) and education level (p = 0.016), and in the dementia cohort for body mass index (p = 0.041); with no significant differences for other covariates. When examining mortality prediction models across the five
study cohorts, we conducted a comparison of fairness evaluations before and after mitigating systematic differences. We revealed significant differences in the CHF cohort with p-values of 0.021 and 0.001 in terms of F1 measure and Sensitivity for the AdaBoost model, and p-values of 0.014 and 0.003 in terms of F1 measure and Sensitivity for the MLP model, respectively.
CONCLUSIONS: This
study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.