AI for Transplant healthcare

https://www.flickr.com/photos/158301585@N08/43267970672

Funded Projects

  1. 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

  2. 2022-2023. Mid-America Transplant, Deep Learning Model to Improve Tissue Authorization for Mid-America Transplant Services, Total Award: $46,763, Co-PI

  3. 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

  4. 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

  1. 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)

  2. 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)

  3. 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.

  4. 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

  5. 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

  6. 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)

  7. 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