.png)
Bridging Genomics and Real-World Data for AI/ML based prediction of Liver graft longevity
The Challenge
Despite improvements in primary immunosuppressive therapies, long-term survival after liver transplantation has not seen significant progress over the past four decades. The client faced challenges in accurately predicting long-term liver graft survival and optimizing donor-recipient compatibility. The existing Liver Donor Risk Index (LDRI) model did not accurately reflect post-transplant liver graft survival, nor did it account for recipient-specific factors.
Our Solution
Agilisium developed an advanced analytics solution leveraging AI/ML models designed to enhance donor-recipient mapping and improve graft survival rates. This solution focuses on the following key aspects:
Exploratory Data Analysis (EDA)
Performed EDA on liver transplant data to identify the clinical variables that influence post-transplant liver graft survival.
Enhanced LDRI Model
Integrated both donor characteristics and recipient factors to provide a more accurate prediction model of liver graft outcomes.
Cytokine Profiling
Analyzed the effect of cytokine profiles in the Early Allograft Dysfunction (EAD) presentation, providing insights into the immune response post-transplant.
Ensemble Classifier Development
Developed an ensemble classifier to predict the relative risk of graft failure based on various clinical and molecular factors.
The Outcomes
Improved Predictive Accuracy
Enhances the accuracy of predicting liver graft survival, allowing for better donor-recipient matching.
Better Patient Outcomes
Reduces the risk of unsuccessful transplants, thereby improving the use of scarce liver grafts.
Comprehensive Reporting
Delivers detailed reports on model evaluation, validation, and cytokine impact, providing actionable insights for clinical decision-making.
Accelerated Research and Development
Aids in development of new transplant methodologies and improving patient care.
Similar Case Studies
%20(5).png)
%20(4).png)
