ArticleScientific reports2026
In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomes.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Pathogen-specific host responses define distinct pneumonia endotypes in the human lung.bioRxiv : the preprint server for biology · 2026Article
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19 authors.
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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 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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