SBIR Phase I: Improved Maternal Health with Predictive Patient Monitoring

这个小企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是改善与先兆子痫相关的临床结局，先兆子痫是一种可能危及生命的妊娠状况，发生在妊娠中期至产后期间。在美国，每年每20个新生儿中就有1个受到影响，即15万名妇女，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，估计每年花费2 + B美元;全球每年有7万人因此丧生。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。这个小企业创新研究（SBIR）第一阶段项目将研究和开发一个预测模型，使用机器学习在最早阶段检测先兆子痫，通过收集患者提供的临床数据来通知一种新的算法，准确率达到80%以上。该奖项反映了NSF的法定使命，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.