Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning

Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning

Abstract Background Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient’s 180-day GIB hospitalization risk. Methods An eXtreme Gradient Boosting (XGBoost) and logistic regression model were...

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Main Authors: John W. Larkin, Suman Lama, Sheetal Chaudhuri, Joanna Willetts, Anke C. Winter, Yue Jiao, Manuela Stauss-Grabo, Len A. Usvyat, Jeffrey L. Hymes, Franklin W. Maddux, David C. Wheeler, Peter Stenvinkel, Jürgen Floege, on behalf of the INSPIRE Core Group
Format: Article
Language:English
Published: BMC 2024-10-01
Series:BMC Nephrology
Subjects:
Bleeding
Gastrointestinal
Hospitalization
Kidney Failure
Predictive Modeling
Online Access:https://doi.org/10.1186/s12882-024-03809-2
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https://doi.org/10.1186/s12882-024-03809-2

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