CAREER: Personalized Maternal Care Decision Support System for Underserved Populations

职业：针对服务不足人群的个性化孕产妇护理决策支持系统

• 批准号: 2339992
• 负责人: Talayeh Razzaghi
• 金额: $ 49.67万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-01 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339992&HistoricalAwards=false
• 关键词: CAREER; Personalized; Maternal; Care; Decision

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.

妇女分娩和妊娠死亡率、孕产妇死亡率被认为是人口健康、社会妇女健康状况以及医疗保健系统本身整体健康状况的重要指标。然而，过去二十年来，美国的孕产妇死亡率不断上升，令人担忧，成为发达国家中最高的。先兆子痫是一种与高血压相关的妊娠并发症。每年，先兆子痫困扰着 8-10% 的美国孕妇，除非在怀孕早期发现并治疗，否则可能导致孕产妇和/或新生儿死亡。识别患有先兆子痫的风险较高的女性仍然是一个挑战，因为有几个因素，特别是年龄、种族和孕前疾病史，可能会导致这种情况的发生。该项目将构建创新技术，使计算机能够理解和预测女性在怀孕期间患先兆子痫的可能性，特别是少数族裔女性。构建这样的系统需要大量数据（从人口统计到个人健康记录）来训练计算机预测先兆子痫。该项目的主要新颖之处在于它能够从通常不完美的临床数据中学习，记录缺失或不完整，有关先兆子痫病例的信息可能很少，并且对包括美洲原住民在内的不同种族群体的亚群保持公平。当预测先兆子痫的风险时。该项目开发的技术还将有可能帮助构建有助于早期发现其他疾病的工具。该项目研究开发基于机器学习 (ML) 的新型临床决策辅助工具，以早期检测先兆子痫 (PE)。该项目的主要新颖之处在于它能够有效解决从 PE 数据集学习所特有的几个问题，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 I）PE 数据集经常面临固有的阶层不平衡； （挑战二）构建用于早期PE检测的可靠ML模型需要挖掘大量且多样化的数据集，例如电子健康记录，这对现有ML模型的可扩展性构成重大挑战； （挑战三）PE 不成比例地影响某些种族群体，特别是美洲印第安人/美洲原住民女性，由于这些差异，将此类 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，研究者将（1）开发一类新的无参数分类器，以有效解决类别不平衡造成的偏差，从而消除计算成本高昂的超参数调整的需要，这是成本敏感的学习模型的常见问题班级不平衡； （2）通过将学习任务制定为顺序决策过程，指导分类中的数据采样，开发一种新颖的可扩展分类方法，用于从大规模PE数据集中学习； (3)开发一类基于易于处理的优化模型的公平分类器，平衡公平性和准确性，以及新颖的性能公平性指标，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如何在一个可以处理海量数据的新型可扩展框架内调整公平的机器学习模型以适应在线学习环境。所提出的基于机器学习的 PE 检测模型的成功实施将增强对先兆子痫高风险孕妇的识别，同时减少相关孕产妇健康管理系统中的种族偏见。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。