Early prediction of long hospital stay for Intensive Care units readmission patients using medication information.

ABSTRACT:

Objective

Predicting Intensive Care Unit (ICU) Length of Stay (LOS) accurately can improve patient wellness, hospital operations, and the health system's financial status. This study focuses on predicting the prolonged ICU LOS (≥3 days) of the 2nd admission, utilizing short historical data (1st admission only) for early-stage prediction, as well as incorporating medication information.

Materials and methods

We selected 18,572 ICU patients' records from the MIMIC-IV database for this study. We applied five machine learning classifiers: Logistic regression (LR), Random Forest (RF), Support Vector Machine (SVM), AdaBoost (AB) and XGBoost (XGB). We computed both the sum dose and the average dose for the medication and included them in our model.

Results

The performance of

SUBMITTER: Zhang M 

PROVIDER: S-EPMC11385457 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature