Developing an Unbiased Machine Learning Tool for Prediction of Acute Coronary Syndrome

摘要 意义：种族和性别差异在诊断和护理急性冠状动脉综合征(ACS)患者中的作用 都有很好的记录。随着机器学习算法(MLA)在医疗保健环境中变得越来越普遍，它是 必须确保这些方法不会通过有偏见的预测或 不同种族和性别群体的不同准确性。研究问题：MLA可以被训练成更多 与通常使用的用于预测急性冠脉综合征的风险分层系统相比，精确度和更少的偏见？之前的工作： 研究小组开发了用于心肌梗死(MI)预测的初步梯度增强树模型 使用电子健康记录中的回溯数据。在一个坚持测试集上，算法分类器达到了 测试时，接收器工作特性曲线(AUROC)值为0.92的区域 在患者住院期间的任何时间点检测MI。相关研究团队之前的其他工作 制定MLA以最小化白人和非白人之间住院死亡率预测的偏差 病人组。该模型被发现是无偏的，以平等机会差(EOD= 0.016，p=0.204)，并且优于常用的严重性评分系统MEWS、SAPS-II和APACHE 在偏差和准确性方面。具体目标：在目标1中，早期急性冠脉综合征预测的无偏模型将是 发展起来的。对MLA训练数据进行预处理将删除反映系统健康的数据方面 在维护反映相关患者测量和结果的数据方面的同时，对不公平现象进行评估。 机会均等差异评估(EOD)和Zemel统计将提供一种评估 MLA的运作能力，没有性别或种族偏见。在目标2中，模型的性能将与三个模型进行比较 常用的急性冠脉综合征危险分层评分。评估模型性能和对这些系统的偏差 将允许将无偏见的MLA与当前的ACS护理标准进行比较。方法：目标1：一个ACS系统 当比较White的性能精度时，将被证明是无偏的预测算法 将开发急诊科患者与非白人和男性与女性的对比。模特的 性能将根据EOD和Zemel统计数据进行评估，这两个统计数据衡量了 假阴性结果和平均预测风险分别在白人和非白人以及男性和 女性患者在零假设下无差异。目标2：将模型性能与 其他三种常用的急性冠脉综合征风险分层评分的修订版：急性冠脉综合征全球注册中心 冠状动脉事件(GRACE)评分；不稳定型心绞痛患者的血小板糖蛋白IIb/IIIa：受体抑制 应用依替菲替林治疗(追踪)评分和心肌梗死溶栓治疗(TIMI) 分数，其中一些已经被证明在性别和种族上表现不同。EOD和Zemel 统计数据也将被评估为MLA、Grace、Purchage和TIMI分数的偏差衡量标准。未来 指导：MLA将在现场医院环境中实施，以进行前瞻性评估。

Abstract Significance: Racial and sex disparities in the diagnosis and care of acute coronary syndrome (ACS) patients are well documented. As machine learning algorithms (MLA) become more common in healthcare settings, it is imperative to ensure that these methods do not contribute to disparities through biased predictions or differential accuracy across racial and sex groups. Research Question: Can a MLA be trained to be more accurate and less biased than commonly used risk stratification systems for ACS prediction? Prior Work: The research team developed a preliminary gradient boosted tree model for myocardial infarction (MI) prediction using retrospective data from electronic health records. On a hold-out test set, the algorithm classifier attained an area under the receiver operating characteristic curve (AUROC) value of 0.92 when tested for the detection of MI at any point during a patient’s hospital stay. Other prior work by the research team involved development of a MLA to minimize bias in inpatient mortality predictions between White and non-White patient groups. The model was found to be unbiased as measured by the equal opportunity difference (EOD = 0.016, p = 0.204) and outperformed commonly used severity scoring systems MEWS, SAPS-II, and APACHE in respect to bias and accuracy. Specific Aims: In Aim 1, an unbiased model for early ACS prediction will be developed. Preprocessing the MLA training data will remove aspects of the data that reflect systemic health inequities while maintaining the aspects of the data that reflect relevant patient measurements and outcomes. Assessment of equal opportunity difference (EOD) and the Zemel statistic will provide a means to evaluate the MLA’s ability to operate without sex or racial bias. In Aim 2, the model’s performance will be compared to three commonly used ACS risk stratification scores. Evaluating model performance and bias against these systems will allow for comparison of the unbiased MLA to the current ACS standard of care. Methods: Aim 1: An ACS prediction algorithm that will be demonstrated to be unbiased when comparing performance accuracy on White vs. non-White and male vs. female emergency department patients will be developed. The model’s performance will be assessed with regard to the EOD and Zemel statistic, which measure the difference in false negative results and average predicted risk, respectively, between White and non-White and male and female patients under the null hypothesis of no difference. Aim 2: Model performance will be compared to modified versions of three other commonly used ACS risk stratification scores: the Global Registry of Acute Coronary Events (GRACE) score; the Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor Suppression Using Integrilin (eptifibatide) Therapy (PURSUIT) score; and the Thrombolysis in Myocardial Infarction (TIMI) score, some of which have been shown to perform differentially across gender and race. EOD and the Zemel statistic will also be assessed as a measure of bias for the MLA, GRACE, PURSUIT and TIMI scores. Future Directions: The MLA will be implemented in live hospital settings for prospective evaluation.