ENGRAFT's work in transplantation is centered on leveraging advanced data science and AI techniques to improve outcomes for transplant patients.
In liver transplantation, Dr. Pruinelli and her team are developing predictive models to assess patient risk, optimize transplant timing, and enhance transplant care over time. By analyzing vast datasets, our overall project identifies key factors influencing transplant success, such as patient health trajectories and organ viability, focusing on conditions ameanable of change with tailored interventions.
Similarly, this same approach is now being expanded to kidney and pancreas transplantation, and we aim to have a better understanding of how data can inform better decision-making and transplantation success for these organs as well.
Our innovative approach aims to personalize treatment plans, reduce complications, and increase survival rates for transplant recipients, making significant contributions to the field of transplantation medicine. Ultimately, we aim for building models where transplant-related benefit can be estimated and quantified while being patient-centric.
Try MELDPredict Tool: A predictive tool to forecast the Model for End-Stage Liver Disease score (Nguyen et. al, 2025).
Selected References
Nguyen, M., Zhou, J., Ma, S., Simon, G., Olson, S., Pruett, T., & Pruinelli, L. (2025). A predictive tool to forecast the Model for End-Stage Liver Disease score: the “MELDPredict” tool. Journal Of Medical Artificial Intelligence, 0. doi:10.21037/jmai-24-277
Pruinelli, L., Balakrishnan, K., Ma, S., Li, Z., Wall, A., Lai, J. C., Schold, J. D., Pruett, T., & Simon, G. (2025). Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review. BMC Medical Informatics and Decision Making, 25(1), 98. https://doi.org/10.1186/s12911-025-02890-3
Balakrishnan, K., Olson, S., Simon, G., & Pruinelli, L. (2024). Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features. Computer Methods and Programs in Biomedicine, 257, 108442. https://doi.org/10.1016/j.cmpb.2024.108442
Ledo, G. V. A., Lozada-Perezmitre, E., Pruinelli, L., Gomez Flores, M. I., & Ramirez-Gonzalez, L. R. (2024). Validation of Educational Materials for a Mobile Application for Patients with Kidney Disease on Hemodialysis. Studies in Health Technology and Informatics, 315, 114–118. https://doi.org/10.3233/SHTI240117
Ledo, G. V. A., Lozada-Perezmitre, E., Pruinelli, L., Landeros-Olvera, E., & Gomez Flores, M. I. (2024). Mobile Applications in Patients with Chronic Kidney Disease: A Systematic Review. Studies in Health Technology and Informatics, 315, 380–385. https://doi.org/10.3233/SHTI240174
Menna Barreto, L. N., Cabral, E. M., Buffon, M. R., Mauro, J. E. P., Pruinelli, L., & de Abreu Almeida, M. (2022). Nursing Diagnosis for Potential Organ Donors: Accuracy Study. Clinical Nursing Research, 31(1), 60–68. https://doi.org/10.1177/10547738211019435
Lentobarros, D., Karp, S., Simon, G. J., Pruett, T., Schold, J., & Pruinelli, L. (2021). Disparity in Access to Care Impacts Liver Transplant Mortality. Studies in Health Technology and Informatics, 284, 209–214. https://doi.org/10.3233/SHTI210706
Pruinelli, L., Zhou, J., Stai, B., Schold, J. D., Pruett, T., Ma, S., & Simon, G. (2021). A likelihood-based convolution approach to estimate major health events in longitudinal health records data: an external validation study. Journal of the American Medical Informatics Association: JAMIA, 28(9), 1885–1891. https://doi.org/10.1093/jamia/ocab087
Pruinelli, L., Simon, G. J., Monsen, K. A., Pruett, T., Gross, C. R., Radosevich, D. M., & Westra, B. L. (2018). A Holistic Clustering Methodology for Liver Transplantation Survival. Nursing Research, 67(4), 331–340. https://doi.org/10.1097/NNR.0000000000000289
Pruinelli, L., Monsen, K. A., Gross, C. R., Radosevich, D. M., Simon, G. J., & Westra, B. L. (2017). Predictors of Liver Transplant Patient Survival. Progress in Transplantation (Aliso Viejo, Calif.), 27(1), 98–106. https://doi.org/10.1177/1526924816680099
Pruinelli, L., Monsen, K. A., Simon, G. J., & Westra, B. L. (2016). Clustering the Whole-Person Health Data to Predict Liver Transplant Survival. Studies in Health Technology and Informatics, 225, 382–386
Pruinelli, L., & Luce Kruse, M. H. (2012). [The media and organ donations: the production of donating subjects]. Revista gaucha de enfermagem, 33(4), 86–93. https://doi.org/10.1590/s1983-14472012000400011