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/族裔差异是持续存在的，PTB的患病率较高 非西班牙裔（N-H）的黑人女性比其N-H白色对应物。但是， 这种黑白差异尚不清楚。甚至广泛的生物医学，行为和社会 - 人口统计风险因素只能解释大约一半的PTB事件。慢性压力已获得重大 注意作为PTB的强大预测指标，尤其是在种族/族裔少数群体中。然而，文学 显示出关于种族/种族，慢性压力和PTB之间关系的不一致的证据，主要是 由于评估妇女的慢性应激暴露涉及的复杂性。准确的慢性应激 措施应捕获压力源的性质：累积，互动和特定于人群。在这个 考虑到常规统计模型（例如，线性回归）模拟慢性应激的能力有限 高精度的暴露。这是这项研究将采用机器学习（ML），这是一种最先进的建模 技术，以计算慢性应激源之间的非线性和协同关系，检测未知 模式，并反映出N-H White和N-H黑人妇女之间慢性应激源的细微差异 准确预测其PTB风险。我将开发简单，准确且可解释的慢性ML算法 通过构建针对N-H White和N-H黑人妇女的混合算法和计算，通过构建一种混合算法来暴露压力 Shap（Shapley添加说明）值。具体而言，混合算法将结合多变量 自适应回归花纹（MARS）和深神经网络（DNN）算法将选择火星 每种种族/种族的唯一“重要”的慢性压力变量，才能充当DNN的投入特征PTB风险 预言。此外，最终算法中每个慢性应激源的外形值将量化其的程度 对预测的PTB风险的贡献。 ML算法将在一个大型国民上进行培训和测试 数据库 - 怀孕风险评估监测系统（2012-2017），由美国37个州收集。这 研究的具体目的是1）比较逻辑回归（LR）和两个ML算法之间的准确性 （DNN和Hybrid）的慢性应激暴露，以预测使用接收器下的区域的PTB风险 特征曲线（AUC）； 2）比较种族/种族兼容与种族/种族之间的准确性 - LR，DNN和混合算法中的特定模型； 3）确定重要性的程度 使用回归系数在表现最佳算法中预测PTB风险的长期应激源（用于 LR）或SHAP值（对于ML算法）。职业发展目标是1）发展压力方面的专业知识 在产妇和儿童健康的背景下进行测量，2）获得ML和ML的知识和技能 大规模数据的分析，以及3）培养以健康信息为中心的手稿和授予准备技能 独立。这项研究的结果将有助于预防弱势孕妇的PTB 通过早期筛查，具有更准确，数据信息的工具来评估这些患者的慢性压力。