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In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomes

Abstract Lung transplantation is a life-saving therapy for end-stage lung disease but has the poorest survival among solid organ transplants. We analyzed standardized electronic health record (EHR) data from the United Network for Organ Sharing (UNOS) to predict one-, three-, and five-year survival...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Lucy Luo, Marcin Możejko, Nikolay S. Markov, Alec Peltekian, Suror Mohsin, Mary Carn, Phillip Cooper, Jeffrey Lysne, Anthony Joudi, Alan Betensley, Bradford C. Bemiss, Catherine Myers, Ankit Bharat, Rade Tomic, Ambalavanan Arunachalam, Ewa Szczurek, G. R. Scott Budinger, Alexander V. Misharin, Mrinalini Venkata Subramani
Format: Artigo
Sprache:Inglês
Veröffentlicht: Nature Portfolio 2026-01-01
Schriftenreihe:Scientific Reports
Online-Zugang:https://doi.org/10.1038/s41598-025-33809-7
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