SBIR Phase I: Improved Maternal Health with Predictive Patient Monitoring

这项小企业创新研究（SBIR） I期项目的更广泛影响/商业潜力是改善与先兆子痫相关的临床结果，先兆子痫是发生在妊娠中期至产后期间的一种可能危及生命的妊娠状况。在美国，每年每20个新生儿中就有1人罹患此病，也就是15万名妇女罹患此病，弱势妇女的死亡率增加了2-3倍。在美国，先兆子痫使孕妇的护理费用增加了两倍，估计每年要花费2亿多美元；在全球范围内，每年夺去7万人的生命。目前还没有办法预测这种情况的发生。该项目提出了预测发病的软件，以改善临床结果。这项小企业创新研究（SBIR）第一阶段项目将研究和开发一种准确率超过80%的预测模型，通过收集患者提供的临床数据来为一种新的算法提供信息，利用机器学习在早期阶段检测子痫前期。它可以从最早发现到产后恢复期使用，可以为临床医生和患者提供模型和警报。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve clinical outcomes associated with preeclampsia, a potentially life-threatening condition of pregnancy occurring in the second trimester through the postpartum period. It annually affects 1 in 20 births, or 150,000 women in the U.S.A., with disadvantaged women experiencing a 2-3x increase in mortality. In the US preeclampia triples the cost of care for pregnant women, costing an estimated $2+ B per year; globally it costs 70,000 lives annually. Currently there is no way of predicting the onset of this condition. This project proposes software to predict onset to improve clinical outcomes. This Small Business Innovation Research (SBIR) Phase I project will research and develop a predictive model with 80%+ accuracy using machine learning to detect preeclampsia in the earliest stages by gathering patient-provided clinical data to inform a novel algorithm. It can be used from earliest detection through the postpartum recovery period and can provide models and alerts to both clinicians and patients.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.