arXiv:2608. 03017v1 Announce Type: new Abstract: There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails.
By Misaki Matsuura, Mohammadreza Nemati, Dulat Bekbolsynov, Stanislaw Stepkowski, Kevin S. Xu
arXiv:2507. 07339v2 Announce Type: replace-cross Abstract: Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc.
By Yingtao Luo, Reza Skandari, Carlos Martinez, Arman Kilic, Rema Padman
arXiv:2606. 02671v1 Announce Type: cross Abstract: Machine learning predictors have become essential tools for guiding automated decision making.
By Itai Zilberstein, Ioannis Anagnostides, Tuomas Sandholm
arXiv:2607. 15380v1 Announce Type: cross Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities.
By Ajay Madhavan Ravichandran, Bilgin Osmandoja, Klemens Budde, Klaus Netter, Tobias Strapatsas, Aljoscha Burchardt, Sebastian M\"oller, Roland Roller
arXiv:2609.07610v1 Announce Type: new
Abstract: We convert black-box clinical prediction models for tabular data into standalone nomograms that can be audited term by term. PRiSM (Partial Responses i...
By Henry Pigot, Paulo J. G. Lisboa, Sandra Ortega-Martorell, Ivan Olier, Joseph Mahon, Johan Nilsson
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
By Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos