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

针对早产风险的慢性压力暴露的种族/民族特定算法：机器学习方法

• 批准号: 10448093
• 负责人: Sangmi Kim
• 金额: $ 14.45万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-11 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10448093
• 关键词: 37 weeks gestation; Adopted; Adverse effects; Algorithms; Area; Artificial Intelligence; Attention; Behavioral; Birth; Birth Rate; Black race; Child Health; Chronic; Chronic stress; Clinical; Complement; Complex; Computers; Data; Databases; Early Intervention; Ethnic Origin; Exposure to; Family; Female of child bearing age; Financial Hardship; Goals; Grant; Health Promotion; Healthcare; High Prevalence; Hybrids; Incidence; Infant; Instruction; Knowledge; Learning; Linear Regressions; Link; Literature; Logistic Regressions; Machine Learning; Manuscripts; Maternal and Child Health; Measurement; Measures; Medical Care Costs; Methodology; Methods; Modeling; Monitor; Nature; Not Hispanic or Latino; Outcome; Patients; Pattern; Population; Populations at Risk; Pregnancy; Pregnant Women; Premature Birth; Preparation; Public Health Informatics; ROC Curve; Race; Research Design; Risk; Risk Assessment; Risk Factors; Sampling; Statistical Models; Stress; Study models; System; Techniques; Testing; Time; Training; Variant; Violence; Woman; base; black women; black/white disparity; career development; community setting; deep neural network; early screening; ethnic diversity; ethnic minority population; experience; large scale data; machine learning algorithm; machine learning model; maternal risk; multidimensional data; neural network algorithm; novel; peer; prevent; racial and ethnic; racial and ethnic disparities; risk prediction; skills; social; sociodemographics; stressor; tool

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.

在美国，早产（PTB）的种族/民族差异持续存在，PTB的患病率较高， 非西班牙裔（N-H）黑人女性比他们的N-H白色同行。然而， 这样的黑白差异还没有得到很好的理解。即使是广泛的生物医学，行为学和社会学- 人口学危险因素只能解释约一半的PTB发病率。慢性压力已经收到了显着的 注意力是PTB的一个强有力的预测因素，特别是在种族/少数民族群体中。然而，文学 在种族/民族、慢性压力和PTB之间的关系方面显示了不一致的证据，主要是 因为评估女性长期压力暴露的复杂性。准确的慢性压力 衡量标准应反映压力源的性质：累积性、互动性和针对具体人群。在这 关于这一点，传统的统计模型（例如，线性回归）对慢性压力建模的能力有限 曝光精度高。因此，本研究将采用机器学习（ML），一种最先进的建模方法， 技术，计算慢性压力源之间的非线性和协同关系，检测未知 模式，并反映了N-H白色和N-H黑人妇女之间的慢性压力源的微妙差异， 准确预测PTB风险。我将开发简单，准确和可解释的慢性疾病的ML算法。 通过建立一个针对N-H白色和N-H黑人女性的混合算法， SHAP（SHapley加法解释）值。具体而言，混合算法将联合收割机多变量 自适应回归样条（MARS）和深度神经网络（DNN）算法，其中MARS将选择 每个种族/民族只有“重要的”慢性压力源变量作为DNN的PTB风险输入特征 预测.此外，最终算法中每个慢性应激源的SHAP值将量化其应激程度。 对预测的PTB风险的贡献。机器学习算法将在一个大型的国家实验室上进行训练和测试。 数据库-妊娠风险评估监测系统（2012-2017）-由美国37个州收集。的 研究的具体目的是：1）比较逻辑回归（LR）和两种ML算法之间的准确性 (DNN和混合）的慢性应激暴露，以预测肺结核的风险，使用面积下的接收器操作 特征曲线（AUC）; 2）比较种族/民族组合和种族/民族之间的准确性- LR、DNN和混合算法中的特定模型;以及3）确定 慢性压力源预测PTB风险的最佳性能算法使用回归系数（对于 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技能 在孕产妇和儿童健康的背景下进行测量，2）获得ML和 大规模数据分析，3）培养以健康信息学为重点的论文和基金准备技能 为了独立这项研究的结果将有助于预防脆弱孕妇的PTB 通过早期筛查，使用更准确的数据信息工具来评估这些患者的慢性压力。