CAREER: Personalized Maternal Care Decision Support System for Underserved Populations

妇女在分娩和怀孕中的死亡率，即孕产妇死亡率，被认为是人口健康、妇女在社会中的健康状况以及医疗保健系统本身整体健康状况的一个重要指标。然而，美国在过去二十年中经历了令人担忧的孕产妇死亡率上升，导致美国达到发达国家中最高的比率。先兆子痫是一种与高血压有关的妊娠并发症。每年，先兆子痫折磨8-10%的美国孕妇，除非在怀孕早期发现和治疗，否则可能导致产妇和/或新生儿死亡。它仍然是一个挑战，以确定妇女在先兆子痫的风险较高，因为几个因素，特别是年龄，种族和孕前疾病的历史，可以有助于发展的条件。该项目将建立创新技术，使计算机能够理解和预测妇女在怀孕期间患先兆子痫的可能性，特别是少数民族妇女。建立这样一个系统需要大量的数据，从人口统计到个人健康记录，来训练计算机预测先兆子痫。该项目的主要新奇之处在于它能够从临床数据中学习，这些数据往往是不完善的，可能缺少或不完整的记录，关于先兆子痫病例的信息可能很少，并且在预测先兆子痫的风险时，对包括美洲原住民在内的各种种族群体的亚群保持公平。该项目开发的技术也将有可能帮助建立有助于早期发现其他疾病的工具。该项目研究开发新的基于机器学习（ML）的临床决策辅助工具，用于早期检测先兆子痫（PE）。该项目的主要新奇在于其能够有效地解决从PE数据集学习的几个特定问题，如果不解决这些问题，将继续阻碍基于ML的PE早期检测的临床实施：（挑战一）PE数据集往往面临固有的类不平衡;（挑战二）为早期PE检测构建可靠的ML模型需要挖掘大型和多样化的数据集，例如电子健康记录，对现有ML模型的可扩展性提出了重大挑战;（挑战III）PE不成比例地影响某些种族群体，特别是美国印第安人/美洲原住民妇女，由于这些差异，将这种ML模型的公平性变成了伦理问题，并对采用ML进行疾病检测提出了挑战。为了应对这些挑战，研究人员将（1）开发一类新的无参数分类器，以有效地解决类别不平衡导致的偏差，从而消除对计算昂贵的超参数调整的需要，这是用于类别不平衡的成本敏感学习模型的常见问题;（2）开发一种新的可扩展分类方法，用于从大规模PE数据集学习，通过将学习任务制定为顺序决策过程，引导分类中的数据采样;以及（3）开发一类基于易处理的优化模型的公平分类器，其平衡公平性和准确性以及新颖的性能公平性度量以同时测量不平衡数据的公平性和准确性。研究人员进一步研究了在一个可以处理大量数据的新型可扩展框架内将公平ML模型应用于在线学习环境。成功实施拟议的ML为基础的PE检测模型将提高识别孕妇在先兆子痫的高风险，同时减少种族偏见，在相关孕产妇健康管理systems.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

The rate of women dying in childbirth and pregnancy, maternal mortality, is recognized as a crucial indicator of population health, the status of women's health in the society, and the overall health of the healthcare system itself. However, the US has experienced a worrying increase in maternal mortality over the last two decades, resulting in the US reaching the highest rate among developed countries. Preeclampsia is a pregnancy complication related to high blood pressure. Each year, preeclampsia afflicts 8-10% of US pregnancies and can lead to maternal and/or neonatal death unless it is detected and treated in early stages of the pregnancy. It remains a challenge to identify women at higher risk of preeclampsia, as several factors, notably age, race, and the history of pre-pregnancy diseases, can contribute to developing the condition. This project will build innovative technologies to allow computers to understand and predict the likelihood of a woman developing preeclampsia during pregnancy, particularly among women from minority racial groups. Building such a system requires massive data, ranging from demographic to individual health records, to train the computers to predict preeclampsia. The main novelty of this project is in its capacity to learn from clinical data that are often imperfect, suffering from missing or incomplete records with possibly very little information on preeclampsia cases, and to remain fair toward subpopulations of various racial groups, including Native Americans, when predicting the risk of preeclampsia. The technologies developed in this project will also have the potential to help build tools that can help in early detection of other diseases. This project investigates developing novel machine learning (ML)-based clinical decision aid tools for early detection of preeclampsia (PE). The main novelty of this project is in its capacity to effectively address several issues specific to learning from PE datasets that, if not addressed, continue to impede the clinical implementation of ML-based early detection of PE: (Challenge I) PE datasets often face inherent class imbalance; (Challenge II) Constructing reliable ML models for early PE detection necessitates mining large and diverse datasets, such as electronic health records, posing a significant challenge to the scalability of existing ML models; and (Challenge III) PE disproportionately affects certain racial groups, notably American Indian/Native American women, turning the fairness of such ML models into an ethical concern due to these disparities, and posing a challenge in adopting ML for disease detection. In response to these challenges, the investigator will (1) develop a new class of parameter-free classifiers to effectively address the bias resulting from class imbalance, thus eliminating the need for computationally expensive hyperparameter tuning, a common issue with cost-sensitive learning models for class imbalance; (2) develop a novel scalable classification method for learning from large-scale PE datasets through formulating the learning task as a sequential decision-making process, guiding data sampling in classification; and (3) develop a class of fair classifiers based on tractable optimization models that balance fairness and accuracy as well as novel performance-fairness metrics to simultaneously measure fairness and accuracy for imbalanced data. The investigator further studies adapting the fair ML model for online learning settings within a novel scalable framework that can handle massive data. Successful implementation of the proposed ML-based PE detection models will enhance identification of pregnant women at a high risk of preeclampsia, while reducing racial biases in relevant maternal health management systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.