Improving Assessment in Kidney Transplantation by Multitask General Path Model

Kidney transplantation helps end-stage patients regain health and quality-of-life. The decisions for matching donor kidneys and recipients affect success of transplantation. However, current kidney matching decision procedures do not consider viability loss during preservation. The objective here is to forecast heterogeneous kidney viability, based on historical datasets to support kidney matching decision-making. Six recently procured porcine kidneys were used to conduct viability assessment experiments to validate the proposed multitask general path model. The model forecasts kidney viability by transferring knowledge from learning the commonality of all kidneys and the heterogeneity of each kidney. The proposed model provides exactly accurate kidney viability forecasting results compared to the state-of-the-art models including a multitask learning model, a general path model, and a general linear model. The proposed model provides satisfactory kidney viability forecasting accuracy because it quantifies the degradation information from trajectory of a viability loss path. It transfers knowledge of common effects from all kidneys and identifies individual effects of each kidney. This method can be readily extended to other decision-making scenarios in kidney transplantation to improve overall assessment performance. For example, analytical generalizations gained by modeling have been validated based on needle biopsy data targeting the improvement of tissue extraction accuracy. The proposed model applied in multiple kidney assessment processes in transplantation can potentially reduce the kidney discard rate by providing effective kidney matching decisions. Thus, the increased kidney utilization rate will benefit more patients and prolong their lives.