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

项目摘要/摘要 这项提议的目标是优化使用电子病历的计算方法。 (EMRS)，例如机器学习(ML)模型，用于预测怀孕期间和第一年的抑郁 小型化有色人种妇女的产后抑郁(PND)。大多数ML模型预测产后 基于中产阶级非西班牙裔白人电子病历的抑郁症(PPD)。然而，我们的结果表明， 非西班牙裔黑人女性(NHBW)患抑郁症的比例更高(23%，而美国平均水平为12%) 而早孕期抑郁在NHBW组比PPD组更常见。在这里，我们建议优化 ML模型在PND三个关键方面的应用。首先，我们将使用偏见缓解方法，以限制 它被称为模型预测性能偏差，定义为不同的模型预测结果 某些社会人口变量，如种族/族裔或年龄。其次，我们将开发能够 提供可解释的结果，并为临床干预提供见解。ML模特往往是“黑匣子”， 这使得很难知道与模型结果相关的变量的方向和大小。第三, 目前用于预测PND的基于EMR的ML模型很少包括社区健康社会决定因素(SDoH)。 在个人层面(例如，少数族裔、贫困)和邻里层面(例如，暴力、 获得护理)与PND风险的增加有关。NHBW不成比例地受到 SDoH对健康的负面影响，包括更高的PND和早产风险。尽管它们很重要，SDoH 在使用ML模型评估PND风险时未被考虑，特别是在#年的少数民族妇女中 经历了不成比例的社会和经济困难负担的有色人种。这限制了模型预测 暴露于较高背景风险的女性的表现。我们假设可解释的ML 对来自小规模有色人种妇女的足够数量的EMR记录进行训练的模型 邻里层面的背景因素(社区层面压力源的替代)可以显著改善 高危女性PND的预测。我们的目标是建立一个健壮且可解释的ML框架 结合个人和社区层面的SDoH预测已经成为 在数据建模中很少出现。我们的长期愿景是将可解释的ML模型集成到例程中 PND的早期发现、诊断和治疗的临床护理。我们将利用大型城市OB/GYN 诊所(&gt；70,000名患者)主要为居住在美国的少数族裔有色人种妇女(50%NHBW，30%拉丁裔)提供服务 芝加哥地区。社区背景信息将从美国人口普查局和 芝加哥健康地图集。在目标1中，我们将开发可解释的ML模型来预测高危妇女的PND 急诊室。在目标2中，我们还将邻居级别的SDoH因素纳入到模型构建中。我们的创新和 由电子病历通知的可解释预测模型和邻里环境数据可在 临床护理，以更准确地识别PND风险最大的妇女，并提供预防性干预。

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.