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

基本信息

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
项目类别：
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

项目摘要

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，其中 PTB 患病率较高非西班牙裔 (N-H) 黑人女性比她们的 N-H 白人盟友然而，其根本机制。即使是广泛的生物医学、行为和社会差异，人们也没有很好地理解这种黑人与白人的差异。人口统计学危险因素只能解释约一半的慢性压力所致结核病发病率。注意力作为 PTB 的有力预测因素，特别是在少数种族/族裔群体中。关于种族/族裔、慢性压力和 PTB 之间关系的证据不一致，主要是因为评估女性的慢性压力暴露非常复杂。措施应抓住压力源的性质：累积性、交互性和特定人群。传统的统计模型（例如线性回归）模拟慢性压力的能力有限因此，本研究将采用机器学习（ML）这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 白人和 N-H 黑人女性之间慢性压力源的微妙差异。我将开发简单、准确且可解释的慢性病 ML 算法。通过构建专门针对 N-H 白人和 N-H 黑人女性和计算的混合算法来缓解压力 SHAP（SHApley Additive exPlanations）值具体来说，混合算法将结合多变量。 MARS 将选择自适应回归样条 (MARS) 和深度神经网络 (DNN) 算法每个种族/民族的唯一“重要”慢性压力源变量可作为 DNN 的 PTB 风险输入特征此外，最终算法中每个慢性压力源的 SHAP 值将量化其程度。机器学习算法将在大型国家/地区进行训练和测试。数据库——妊娠风险评估监测系统（2012-2017）——由美国 37 个州收集。研究的具体目标是 1) 比较逻辑回归 (LR) 和两种 ML 算法的准确性慢性压力暴露（DNN 和混合），使用接收器操作下的面积来预测 PTB 风险特征曲线（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；3) 确定重要性的程度使用回归系数（对于 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。孕产妇和儿童健康背景下的测量，2) 获得机器学习和机器学习方面的知识和技能分析大规模数据，3) 培养以健康信息学为重点的手稿和拨款准备技能这项研究的结果将有助于预防弱势孕妇的 PTB。通过使用更准确、基于数据的工具进行早期筛查来评估这些患者的慢性压力。