I-Corps: Digital twin technology via synthetic data generation to predict outcomes of clinical trials

Award ID: 2133778

The broader impact/commercial potential of this I-Corps project is the development of a platform technology that is applicable to fields where experimental data collection is expensive or infeasible. A first application will be in the domain of clinical trials, where there is a growing trend towards precision medicine, yet experimentation with randomized control trials is often extremely costly and time consuming. This is particularly true for mid-sized pharmaceutical companies with more limited resources. The digital twin technology provides companies with synthetic data to help optimize trial design and control risks in how to allocate limited financial and personnel resources. The technology may also have far-reaching applications in potential application areas of e-commerce and precision agriculture. This I-Corps project develops a new causal inference technology to accurately predict outcomes of clinical trials on a patient-by-patient basis. This technology builds a digital twin of each patient using historical clinical data across different patients, treatments, and diseases. A novel benefit of this approach is that despite very scarce data on patients that go through the full clinical trial, the technology can accurately simulate the outcomes of the entire patient population. Additionally, this approach offers interpretability of how such digital twins are created, a critical feature in medical applications. 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: Ruth Shuman