A Flexible and Synthetic Transplant Kidney Exchange Problem (FASTKEP) Data Generator

The Kidney Exchange Problem (KEP) optimizes a pool of incompatible patient-donor pairs and non-directed donors to determine the optimal cycles and chains of length-N that maximize transplants. A direct impact of KEP optimization is an improved quality of life for transplant recipients at a lower cost to the healthcare system (as opposed to dialysis treatments). To improve this process, synthetic data is commonly used to test and develop KEP algorithms. The canonical KEP data generation process is referred to as the Saidman Generator. The generator utilizes attributes of the donors and recipients that determine compatibility, such as blood type and protein compatibility, alongside known data distributions, including the percentage of different blood types comprising an exchange, to create datasets that aim to mirror the real world. Prior published Java implementations of this generator exist. However, in this work, an open-source data generation package implemented in Python is proposed for the KEP which mirrors the Saidman generator’s output. The proposed implementation is compared to a common Java implementation for scalability and efficiency. This generator allows for future extensions that integrate additional recipient and donor features that produce data better matching real-world exchanges. The result is an open-source Python implementation of a KEP data generator. The efficient creation of directed exchange datasets enables future work, including the use of the presented data generation process by ML/AI researchers in application to large-scale machine learning model training.