  The intensive care unit (ICU) comprises a complex hospital environment, where
decisions made by clinicians have a high level of risk for the patients' lives.
A comprehensive care pathway must then be followed to reduce p complications.
Uncertain, competing and unplanned aspects within this environment increase the
difficulty in uniformly implementing the care pathway. Readmission contributes
to this pathway's difficulty, occurring when patients are admitted again to the
ICU in a short timeframe, resulting in high mortality rates and high resource
utilisation. Several works have tried to predict readmission through patients'
medical information. Although they have some level of success while predicting
readmission, those works do not properly assess, characterise and understand
readmission prediction. This work proposes a standardised and explainable
machine learning pipeline to model patient readmission on a multicentric
database (i.e., the eICU cohort with 166,355 patients, 200,859 admissions and
6,021 readmissions) while validating it on monocentric (i.e., the MIMIC IV
cohort with 382,278 patients, 523,740 admissions and 5,984 readmissions) and
multicentric settings. Our machine learning pipeline achieved predictive
performance in terms of the area of the receiver operating characteristic curve
(AUC) up to 0.7 with a Random Forest classification model, yielding an overall
good calibration and consistency on validation sets. From explanations provided
by the constructed models, we could also derive a set of insightful
conclusions, primarily on variables related to vital signs and blood tests
(e.g., albumin, blood urea nitrogen and hemoglobin levels), demographics (e.g.,
age, and admission height and weight), and ICU-associated variables (e.g., unit
type). These insights provide an invaluable source of information during
clinicians' decision-making while discharging ICU patients.
