SBIR Phase I: Improved Maternal Health with Predictive Patient Monitoring

Award ID: 2111902

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.

NSF Program Director: Alastair Monk