@article{f1140304395e4aa8bfbe7ddd7cc54673,
title = "Predicting Post-Heart Transplant Composite Renal Outcome Risk in Adults: A Machine Learning Decision Tool",
keywords = "dialysis, end-stage renal disease, heart transplant, machine learning, prediction, random forest",
author = "Mutlu Mete and Ayvaci, {Mehmet U.S.} and Ariyamuthu, {Venkatesh K.} and Alpesh Amin and Matthias Peltz and Thibodeau, {Jennifer T.} and Grodin, {Justin L.} and Mammen, {Pradeep P.A.} and Sonia Garg and Faris Araj and Robert Morlend and Drazner, {Mark H.} and Nashila AbdulRahim and Yeongin Kim and Yusuf Salam and Gungor, {Ahmet B.} and Bulent Delibasi and Kotla, {Suman K.} and MacConmara, {Malcolm P.} and {Mohan Anand}, Prince and Gaurav Gupta and Bekir Tanriover",
note = "Funding Information: This research is partly supported by the University of Texas System Southwestern The George M. O'Brien Kidney Research Core Center NIH P30DK079328 and the Texas Health Resources Scholarship (JLG). This work was supported in part by Health Resources and Services Administration contract 234-2005-37011C. The content is the responsibility of the authors alone. It does not necessarily reflect the Department of Health and Human Services' views or policies, nor does it mention trade names, commercial products, or organizations imply endorsement by the US Government.",
year = "2022",
month = jun,
doi = "10.1016/j.ekir.2022.04.004",
language = "English (US)",
volume = "7",
pages = "1410--1415",
journal = "Kidney International Reports",
issn = "2468-0249",
publisher = "Elsevier Inc.",
number = "6",
}