Integration of electronic medical records and neighborhood contextual indicators into machine learning strategies for identifying pregnant individuals at risk of depression in underserved communities

将电子病历和社区背景指标整合到机器学习策略中，以识别服务欠缺社区中面临抑郁风险的孕妇

• 批准号: 10741143
• 负责人: YANG DAI
• 金额: $ 41.94万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10741143
• 关键词: Affect; Age; Air Pollution; Area; Atlases; Censuses; Chicago; Child Care; Cities; Clinic; Clinical; Communities; Computerized Medical Record; Computing Methodologies; Data; Development; Diagnosis; Discipline of obstetrics; Disparate; Early Diagnosis; Economics; Elasticity; Ethnic Origin; Exclusion; Exposure to; Food; Gestational Diabetes; Goals; Health; Health Services Accessibility; Healthcare; High Risk Woman; Individual; Intervention; Latina Population; Link; Longitudinal Studies; Low income; Machine Learning; Measures; Mental Depression; Methods; Minority Women; Modeling; Monitor; Mood Disorders; Neighborhoods; Not Hispanic or Latino; Obesity; Outcome; Patient Self-Report; Patients; Performance; Postpartum Depression; Postpartum Period; Poverty; Poverty Areas; Premature Birth; Proxy; Race; Records; Risk; Risk Assessment; Structural Racism; Substance abuse problem; Testing; Training; Transportation; Unemployment; Violence; Woman; Work; antepartum depression; artificial neural network; black women; built environment; clinical care; contextual factors; data modeling; data portal; depressive symptoms; early pregnancy; experience; high risk; high risk population; improved; indexing; innovation; insight; learning strategy; machine learning framework; machine learning model; machine learning prediction; metropolitan; model building; negative affect; outcome prediction; perinatal outcomes; peripartum depression; predictive modeling; pregnant; preventive intervention; racial minority; remediation; segregation; social; social health determinants; sociodemographic variables; stressor; underserved community; unintended pregnancy; urban poverty area; women of color

PROJECT SUMMARY/ABSTRACT The goal of this proposal is to optimize the use of computational methods using electronic medical records (EMRs), such as machine learning (ML) models, to predict depression during pregnancy and the first year postpartum (perinatal depression, PND) in Minoritized Women of Color. Most ML models forecast postpartum depression (PPD) based on EMR from middle class Non-Hispanic White individuals. However, our results show that Non-Hispanic Black Women (NHBW) have higher rates of depression (23% versus the 12% US average) and depression during early pregnancy in NHBW is far more common than PPD. Here, we propose to optimize the application of ML models to PND in three keyways. First, we will use bias-mitigation approaches, to limit what it is called model prediction performance bias, defined as the disparate model prediction outcome with respect to certain socio-demographic variables, such race/ethnicity or age. Second, we will develop ML models that can offer interpretable outcomes and provide insights for clinical interventions. ML models are often “black boxes”, making it difficult to know the direction and magnitude of variables associated with the model outcome. Third, current EMR-based ML models to predict PND rarely include community social determinants of health (SDoH). SDoH both at the individual-level (e.g., racial minority, poverty) and at the neighborhood-level (e.g., violence, access to care) have been linked with increased risk of PND. NHBW are disproportionally affected by the negative health impacts of SDoH, including higher risk of PND and preterm birth. Despite their importance, SDoH have not been considered in assessing risk of PND using ML models, particularly among Minority Women of Color who experience disproportionate burden of social and economic hardship. This limits the model prediction performance in women who are exposed to higher contextual risks. We hypothesize that interpretable ML models trained on sufficient numbers of EMR records from Minoritized Women of Color and that integrate neighborhood-level contextual factors (a proxy for community-level stressors) can substantially improve the prediction of PND in women at higher risk. We aim to establish a robust and interpretable ML framework that combines individual- and community-level SDoH to predict PND for Minoritized Women of Color who have been rarely represented in data modeling. Our long-term vision is to integrate our interpretable ML model into routine clinical care for early detection, diagnosis, and treatment of PND. We will capitalize on large urban OB/GYN clinics (>70,000 patients) primarily serving Minoritized Women of Color (50% NHBW, 30% Latinas) living in the Chicago area. Neighborhood contextualized information will be obtained from the US Census Bureau and the Chicago Health Atlas. In Aim 1, we will develop interpretable ML models to predict PND in at-risk women using EMRs. In Aim 2, we will also incorporate neighbor-level SDoH factors into model building. Our innovative and interpretable prediction models informed by EMR, and neighborhood contextual data could be leveraged in clinical care to identify women more accurately at greatest risk of PND and by informing preventive intervention.

项目概要/摘要 该提案的目标是优化使用电子病历的计算方法的使用 (EMR)，例如机器学习 (ML) 模型，用于预测怀孕期间和第一年的抑郁症 大多数 ML 模型预测有色人种少数女性的产后抑郁症（围产期抑郁症，PND）。 然而，我们的结果显示，基于中产阶级非西班牙裔白人的电子病历（EMR）。 非西班牙裔黑人女性 (NHBW) 的抑郁症发病率较高（美国平均水平为 23%，而美国平均水平为 12%） NHBW 中妊娠早期抑郁症比 PPD 更为常见，在此，我们建议进行优化。 ML 模型在 PND 中的应用有三个关键点：首先，我们将使用偏差缓解方法来限制什么。 它被称为模型预测性能偏差，定义为不同模型的预测结果相对于 其次，我们将开发能够识别某些社会人口变量的机器学习模型。 提供可解释的结果并为临床干预提供见解，机器学习模型通常是“黑匣子”， 使得很难知道与模型结果相关的变量的方向和大小。 当前用于预测 PND 的基于 EMR 的 ML 模型很少包括社区健康社会决定因素 (SDoH)。 SDoH 既针对个人层面（例如，少数族裔、贫困），又针对邻里层面（例如，暴力、 获得护理的机会）与 PND 风险增加有关，NHBW 受到不成比例的影响。 SDoH 对健康产生负面影响，包括较高的 PND 和早产风险，尽管 SDoH 很重要。 在使用 ML 模型评估 PND 风险时尚未考虑，特别是在少数民族妇女中 有色人种经历了不成比例的社会和经济困难负担，这限制了模型的预测。 我们勇敢地面对可解释的机器学习。 模型接受了来自少数族裔女性的足够数量的 EMR 记录的训练，并整合了 邻里层面的背景因素（社区层面压力源的代表）可以显着改善 我们的目标是建立一个强大且可解释的机器学习框架，以预测高危女性的 PND。 结合个人和社区层面的 SDoH 来预测少数族裔有色人种女性的 PND 很少在数据建模中体现出来，我们的长期愿景是将我们的可解释的 ML 模型集成到日常工作中。 我们将利用大型城市妇产科进行早期发现、诊断和治疗的临床护理。 诊所（>70,000 名患者）主要为生活在 芝加哥地区的社区背景信息将从美国人口普查局和人口普查局获得。 在目标 1 中，我们将开发可解释的 ML 模型来预测高危女性的 PND。 在目标 2 中，我们还将 SDoH 因素纳入我们的创新和模型构建中。 由 EMR 提供的可解释预测模型和邻里上下文数据可以用于 临床护理可更准确地识别罹患 PND 风险最高的女性，并提供预防性干预措施。