Race/Ethnicity-Specific Algorithms of Chronic Stress Exposures for Preterm Birth Risk: Machine Learning Approach

在美国，早产(PTB)的种族/民族差异持续存在，在美国，早产的发病率更高 非西班牙裔(N-H)黑人妇女比她们的N-H白人妇女。然而，潜在的机制是 这种黑人和白人的差异并没有得到很好的理解。甚至广泛的生物医学、行为和社会- 人口风险因素只能解释大约一半的肺结核发病率。慢性压力已经受到了显著的 注意力是肺结核的有力预测因素，特别是在种族/少数民族群体中。然而，文学 对种族/民族、慢性压力和肺结核之间的关系显示出不一致的证据，主要是 因为评估女性的慢性压力暴露涉及的复杂性。准确的慢性应激 测量方法应捕捉压力源的性质：累积性、互动性和特定人群。在这 关于这一点，传统的统计模型(例如，线性回归)对慢性应激进行建模的能力有限 高精度的曝光。因此，本研究将采用最先进的建模技术--机器学习(ML 技术，计算慢性应激源之间非线性和协同关系，检测未知 模式，并反映了N-H白人和N-H黑人女性慢性压力源的细微差异，以获得更多 准确预测他们患肺结核的风险。我将开发简单、准确和可解释的慢性ML算法 通过构建专门针对N-H白人和N-H黑人女性的混合算法和计算来应对压力暴露 Shap(Shapley附加解释)值。具体地说，混合算法将结合多变量 MARS将选择的自适应回归样条法(MARS)和深度神经网络(DNN)算法 只有每个种族/民族的“重要”慢性应激源变量才能作为DNN对肺结核风险的输入特征 预测。此外，最终算法中每个慢性应激源的Shap值将量化其程度 对预测的肺结核风险的贡献。ML算法将在一家大型国家 数据库-怀孕风险评估监测系统(2012-2017)--由美国37个州收集。这个 研究的具体目的是：1)比较Logistic回归和两种最大似然算法的准确性 (DNN和混合)慢性应激暴露使用接受者操作下的面积预测肺结核风险 特征曲线(AUC)；2)比较种族/民族组合和种族/民族- LR、DNN和混合算法中的特定模型；以及3)确定 慢性应激源对最佳执行算法中预测的肺结核风险使用回归系数(用于 LR)或Shap值(用于ML算法)。职业发展目标是1)培养应对压力的专业知识 在妇幼保健方面的衡量，2)获得ML和 分析大规模数据，以及3)培养以卫生信息学为重点的稿件和赠款准备技能 为了独立。这项研究的结果将有助于预防易感孕妇的肺结核 通过早期筛查，使用更准确、更有数据信息的工具来评估这些患者的慢性压力。

Racial/ethnic disparities in preterm birth (PTB) are persistent in the U.S., with a higher prevalence of PTB in non-Hispanic (N-H) Black women than their N-H White counterparts. However, the underlying mechanism of such Black-White differences is not well understood. Even extensive biomedical, behavioral, and socio- demographic risk factors can explain only about half of PTB incidence. Chronic stress has received significant attention as a robust predictor of PTB, particularly among racial/ethnic minority groups. Nevertheless, literature shows inconsistent evidence on the relationships among race/ethnicity, chronic stress, and PTB, mainly because of the complexities involved in assessing women’s chronic stress exposures. Accurate chronic stress measures should capture the nature of stressors: cumulative, interactive, and population-specific. In this regard, conventional statistical models (e.g., linear regression) have limited ability to model chronic stress exposures with high precision. Thus, this study will adopt machine learning (ML), a state-of-the-art modeling technique, to compute non-linear and synergistic relationships among chronic stressors, detect unknown patterns, and reflect subtle differences in chronic stressors between N-H White and N-H Black women for more accurate prediction of their PTB risk. I will develop simple, accurate, and explainable ML algorithms of chronic stress exposures by building a hybrid algorithm specific to N-H White and N-H Black women and computing SHAP (SHapley Additive exPlanations) values. Specifically, the hybrid algorithm will combine Multivariate Adaptive Regression Splines (MARS) and Deep Neural Network (DNN) algorithms where MARS will select only “important” chronic stressor variables for each race/ethnicity to serve as DNN’s input features for PTB risk prediction. Additionally, a SHAP value for each chronic stressor in the final algorithm will quantify its degree of contribution to the predicted PTB risk. The ML algorithms will be trained and tested on a large national database—Pregnancy Risk Assessment Monitoring System (2012-2017)—collected by 37 U.S. states. The study’s specific aims are to 1) compare the accuracy among logistic regression (LR) and two ML algorithms (DNN and hybrid) of chronic stress exposures to predict PTB risk using area under the receiver operating characteristic curve (AUC); 2) compare the accuracy between race/ethnicity-combined and race/ethnicity- specific models within LR, DNN, and hybrid algorithms; and 3) determine the extent of the importance of chronic stressors to the predicted PTB risk in the best-performing algorithm using regression coefficients (for LR) or SHAP values (for ML algorithm). Career development goals are to 1) develop expertise in stress measurement in the context of maternal and child health, 2) acquire knowledge and skills in ML and the analysis of large-scale data, and 3) cultivate health informatics-focused manuscript and grant preparation skills for independence. Results from this study will contribute to preventing PTB among vulnerable pregnant women via early screening with more accurate, data-informed tools to assess these patients’ chronic stress.