FutureMatch: Combining Human Value Judgments and Machine Learning to Match in Dynamic Environments

The preferred treatment for kidney failure is a transplant; however, demand for donor kidneys far outstrips supply. Kidney exchange, an innovation where willing but incompatible patient-donor pairs can exchange organs—via barter cycles and altruist-initiated chains—provides a life-saving alternative. Typically, fielded exchanges act myopically, considering only the current pool of pairs when planning the cycles and chains. Yet kidney exchange is inherently dynamic, with participants arriving and departing. Also, many planned exchange transplants do not go to surgery due to various failures. So, it is important to consider the future when matching. Motivated by our experience running the computational side of a large nationwide kidney exchange, we present FUTUREMATCH, a framework for learning to match in a general dynamic model. FUTUREMATCH takes as input a high-level objective (e.g., “maximize graft survival of transplants over time”) decided on by experts, then automatically...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: John P. Dickerson, Tuomas Sandholm
• Keywords: AAAI 2015, Intelligent Agents