AI for Transplant healthcare
Funded Projects
2022-2026. National Science Foundation, Collaborative Research: FW-HTF-R: Embedding Preferences in Adaptable Artificial Intelligence Decision Support for Transplant Healthcare to Reduce Kidney Discard, Total Award: $1,800,000, PI, Collaboration with SLU, UNOS
2022-2023. Mid-America Transplant, Deep Learning Model to Improve Tissue Authorization for Mid-America Transplant Services, Total Award: $46,763, Co-PI
2022-2024. Mid-America Transplant Foundation, AI-Enabled Digital Support to Increase Placement of Hard-to-Place Deceased Donor Kidneys, Total Award: $211,453, Co-PI, Collaboration with SLU
2020-2021. National Science Foundation, FW-HTF-P: Teaming Transplant Professionals and Artificial Intelligence Tools to Reduce Kidney Discard, Total Award: $150,000, PI, Collaboration with SLU
Publications
Threlkeld, R., Ashiku, L., Dagli, C., Dzieran, R., Canfield, C., Lentine, K., Schnitzler, M., Marklin, G., Rothweiler, R., Speir, L., & Randall, H. (2023). AI-Enabled Digital Support to Increase Placement of Hard-to-Place Deceased Donor Kidneys. American Journal of Transplantation. (extended abstract published from American Transplant Congress)
Ashiku, L., Threlkeld, R., Dagli, C., Schnitzler, M., Canfield, C., Lentine, K., & Randall, H. (2022). Donor Disposition AI Model to Predict Transplant for Recovered Deceased Donor Kidneys. American Journal of Transplantation, 22(suppl 3), 652-653. (extended abstract published from American Transplant Congress)
Ashiku, L., Threlkeld, R., Canfield, C., & Dagli, C. (2022). Identifying AI Opportunities in Donor Kidney Acceptance: Incremental Hierarchical Systems Engineering Approach. IEEE Systems Conference (SysCon), pp. 1-8, doi: 10.1109/SysCon53536.2022.9773875.
Elder, H., Canfield, C., Shank, D. B., Rieger, T., & Hines, C. (2022). Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts. Human Factors. https://doi.org/10.1177/00187208221100691
Threlkeld, R., Ashiku, L., Canfield, C., Shank, D., Schnitzler, M., Lentine, K., Axelrod, D., Battineni, A. C. R., Randall, H., Dagli, C. (2021). Reducing Kidney Discard with Artificial Intelligence Decision Support: The Need for a Transdisciplinary Systems Approach. Current Transplantation Reports, 8, 263-271. https://doi.org/10.1007/s40472-021-00351-0
Schnitzler, M. A., Dagli, C., Canfield, C., Dzebisashvili, N., Varma, C., Axelrod, D., Lentine, K., Ouseph, R., & Randall, H. (2020). Using Artificial Intelligence Tools for Identification of High Risk Transplant Recipients for Focused Management. American Journal of Transplantation, 20(suppl 3), 283–284. (extended abstract published from American Transplant Congress)
Subramanian, H. V., Canfield, C., Shank, D. B., Andrews, L., & Dagli, C. (2020) Communicating Uncertain Information from Deep Learning Models in Human Machine Teams. Proceedings of the American Society for Engineering Management.
Media
Article: St. Louis Public Radio - Missouri S&T professor is looking to AI to help with kidney transplants
Press Release: SLU - Together with Missouri S&T, Saint Louis University Researchers Use Artificial Intelligence to Improve Kidney Transplant Process
Press Release: Missouri S&T - Missouri S&T researchers win funding to improve kidney transplant process
Press Release: Missouri S&T - Researchers use artificial intelligence to improve efficiency in kidney transplant network