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DynamITE: Optimal time-sensitive organ offers using ITE
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:696-713, 2025.
Abstract
Matching donor organs to patients in need is a difficult but important problem. A crucial factor in transplant outcomes is the cold ischemic time of the organ, which increases every time an organ offer is refused. Despite this, acceptance dynamics have so far been neglected in favor of purely outcome driven offers. As a first alternative, we propose DynamITE, a novel organ allocation methodology that explicitly takes into account the acceptance behavior over sequences of offers. DynamITE dynamically updates organ acceptance estimates, cold ischemic times (CIT) and causal effects throughout the matching process. We demonstrate that DynamITE improves early organ acceptance and maximizes patient life expectancy compared to current policies.