DynamITE: Optimal time-sensitive organ offers using ITE

Alessandro Marchese, Hans de Ferrante, Jeroen Berrevoets, Sam Verboven
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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v259-marchese25a, title = {DynamITE: Optimal time-sensitive organ offers using ITE}, author = {Marchese, Alessandro and de Ferrante, Hans and Berrevoets, Jeroen and Verboven, Sam}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {696--713}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/marchese25a/marchese25a.pdf}, url = {https://proceedings.mlr.press/v259/marchese25a.html}, 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.} }
Endnote
%0 Conference Paper %T DynamITE: Optimal time-sensitive organ offers using ITE %A Alessandro Marchese %A Hans de Ferrante %A Jeroen Berrevoets %A Sam Verboven %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-marchese25a %I PMLR %P 696--713 %U https://proceedings.mlr.press/v259/marchese25a.html %V 259 %X 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.
APA
Marchese, A., de Ferrante, H., Berrevoets, J. & Verboven, S.. (2025). DynamITE: Optimal time-sensitive organ offers using ITE. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:696-713 Available from https://proceedings.mlr.press/v259/marchese25a.html.

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