Offline Contextual Bandits for Lung Donor–Recipient Matching: A Retrospective Feasibility Study

Background

Lung donor–recipient matching requires balancing recipient medical urgency with expected post-transplant benefit. Conventional prediction models estimate outcomes for historical donor–recipient pairs but do not directly compare alternative candidates for the same donor. We developed an offline contextual-bandit framework to assess the feasibility of learning donor-centered lung-matching policies from historical registry data.

Methods