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.

妇女在分娩和怀孕中死亡的妇女的死亡率，孕产妇死亡，被认为是人口健康的关键指标，社会妇女健康状况以及医疗保健系统本身的整体健康状况。但是，在过去的二十年中，美国的孕产妇死亡率令人担忧，导致美国在发达国家中达到了最高的速度。先兆子痫是与高血压有关的妊娠并发症。每年，先兆子痫遭受了8-10％的美国怀孕，并且可能导致母体和/或新生儿死亡，除非在怀孕的早期被发现和治疗。确定较高的先兆子痫风险的妇女是一个挑战，因为几个因素，尤其是年龄，种族和孕前疾病的病史，可以有助于发展这种疾病。该项目将建立创新的技术，以使计算机能够理解和预测怀孕期间女性发展先兆子痫的可能性，尤其是在来自少数种族群体的妇女中。构建这样的系统需要大量数据，从人口统计到个体健康记录，以训练计算机以预测子痫前期。该项目的主要新颖性是从通常是不完美的临床数据中学习的能力，遭受缺失或不完整的记录，可能很少有关于先兆子痫病例的信息，并且在预测前倾斜的风险时对包括美洲原住民的各种种族群体的亚人群保持公平。该项目开发的技术还将有可能帮助建立可以帮助早期发现其他疾病的工具。该项目研究了开发新型机器学习（ML）基于基于临床的临床决策援助工具，用于早期检测前启示性（PE）。该项目的主要新颖性是有效地解决了从PE数据集中学习的几个问题，如果没有解决，这些问题继续阻碍基于ML的PE的临床实施：（挑战I）PE数据集通常会面临固有的类别不平衡； （挑战ii）为早期PE检测构建可靠的ML模型需要开采大型和多样化的数据集，例如电子健康记录，对现有ML模型的可伸缩性构成了重大挑战； （挑战III）PE不成比例地影响某些种族群体，尤其是美国印第安人/原住民妇女，由于这些差异，此类ML模型的公平性变成了道德上的关注，并在采用ML进行疾病检测时提出了挑战。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，以有效解决阶级失衡导致的偏见，从而消除了对计算昂贵的高参数调整的需求，这是对类别不平衡的成本敏感学习模型的常见问题； （2）开发一种新颖的可扩展分类方法，用于通过将学习任务作为顺序决策过程制定，从大规模的PE数据集中学习，并指导分类中的数据采样； （3）基于可拖动的优化模型开发一类公平的分类器，这些模型平衡了公平和准确性以及新颖的性能 - 费用指标，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在一个可以处理大量数据的新型可扩展框架内适应公平的ML模型，以适应在线学习设置。拟议的基于ML的PE检测模型的成功实施将增强对先兆子痫的高风险的识别，同时减少相关孕妇健康管理系统中的种族偏见。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持，并被认为是值得的。