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Luo, L.* ; Możejko, M.* ; Markov, N.S.* ; Peltekian, A.* ; Mohsin, S.* ; Carn, M.* ; Cooper, P.* ; Lysne, J.* ; Joudi, A.* ; Betensley, A.* ; Bemiss, B.C.* ; Myers, C.* ; Bharat, A.* ; Tomic, R.* ; Arunachalam, A.* ; Szczurek, E. ; Budinger, G.R.S.* ; Misharin, A.V.* ; Subramani, M.V.*

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

Sci. Rep. 16:3699 (2026)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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 and favorable long-term outcomes post-lung transplant. We applied two multivariate machine learning approaches, XGBoost or a tabular BERT model called EHRFormer, to data from 43,869 first-time lung transplant recipients (1987–2022). XGBoost and EHRFormer identified features that align closely with established risk factors for worse outcomes such as length of index stay, recipient age, and creatinine at the time of transplant. We developed a simple perturbation method with EHRFormer to probe in silico multivariate interactions between features that influence model prediction. Despite their attention to known risk factors, machine learning applied to EHR data collected by UNOS poorly predict one-, three-, and five-year mortality after lung transplant.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Adult Lung; Donor; Age; Survival; Impact
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Journal Scientific Reports
Quellenangaben Volume: 16, Issue: 1, Pages: , Article Number: 3699 Supplement: ,
Publisher Springer
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Grants Northwestern Information Technology
Feinberg School of Medicine
Northwestern University Information Technology
Office for Research
Office of the Provost