Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients

Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients

Abstract Background Unplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive for hospitals. This study aims to develop a probability calculator to predict unplanned readmissions (PURE) within 30-days after discharge from the department of Urology, and...

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Bibliographic Details
Main Authors: Koen Welvaars, Michel P. J. van den Bekerom, Job N. Doornberg, Ernst P. van Haarst, OLVG Urology Consortium
Format: Article
Language:English
Published: BMC 2023-06-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Unplanned readmissions
Urology
Machine Learning
Algorithms
XGBoost
Online Access:https://doi.org/10.1186/s12911-023-02200-9

Internet

https://doi.org/10.1186/s12911-023-02200-9

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