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

摘要 意义：急性冠状动脉综合征（ACS）患者诊断和护理中的种族和性别差异 都有详细的记载随着机器学习算法（MLA）在医疗保健环境中变得越来越普遍， 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之间的准确性差异。研究问题：MLA可以被训练成更多 ACS预测的准确性和偏倚性低于常用的危险分层系统？作品：The 一个研究小组开发了一种用于心肌梗死（MI）预测的初步梯度提升树模型 使用电子健康记录的回顾性数据。在一个保持不变的测试集上，该算法的分类器达到了 接受者工作特征曲线下面积（AUROC）值为0.92， 在患者住院期间的任何时间点检测MI。参与研究的研究小组先前的其他工作 制定MLA，以最大限度地减少白色和非白色患者之间住院死亡率预测的偏倚 患者群体。该模型被认为是无偏的，如测量的平等机会差异（EOD = 0.016，p = 0.204），并且优于常用的严重程度评分系统MEWS、SAPS-II和APACHE 在偏差和准确性方面。具体目标：在目标1中，将建立早期ACS预测的无偏模型， 开发预处理MLA训练数据将删除反映系统健康的数据方面 不公平，同时保持反映相关患者测量和结果的数据方面。 平等机会差异（EOD）和Zemel统计的评估将提供一种手段来评估 MLA在没有性别或种族偏见的情况下运作的能力在目标2中，模型的性能将与三个 常用的ACS风险分层评分。评估模型性能和对这些系统的偏见 将允许将无偏见的MLA与当前的ACS护理标准进行比较。方法：目的1：ACS 当比较白色的性能准确度时，将证明预测算法是无偏的 vs.将制定非白人和男性与女性急诊科患者。模型的 性能将根据爆炸物处理和泽梅尔统计进行评估，这两项统计衡量了 白色和非白色和男性之间的假阴性结果和平均预测风险， 女性患者在无差异的零假设下。目标2：将模型性能与 其他三种常用ACS风险分层评分的改良版本：急性冠脉综合征全球登记研究 冠状动脉事件（GRACE）评分;不稳定型心绞痛患者血小板糖蛋白IIb/IIIa：受体抑制 使用Integrilin（依替巴肽）治疗（PURSUIT）评分;和心肌梗死溶栓（TIMI） 分数，其中一些已被证明在性别和种族之间存在差异。爆炸物处理和Zemel 还将评估统计量作为MLA、GRACE、PURSUIT和TIMI评分的偏倚指标。未来 说明：MLA将在现场医院环境中实施，以进行前瞻性评价。

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.