Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Oct 08, 2021)
Date Submitted: Aug 13, 2021
Open Peer Review Period: Aug 13, 2021 - Oct 8, 2021
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A machine learning-based model for predicting intensive care unit length of stay in patients with cardiac surgery
ABSTRACT
Background:
The intensive care unit (ICU) length of stay is significant to evaluate the effect of cardiac surgical treatment inpatient.
Objective:
This research aims to accurately predict the ICU length of stay in patients with cardiac surgery.
Methods:
We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression.
Methods:
We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression.
Results:
The mean accuracy are 0.603 (95% confidence interval (CI): [0.602-0.604]), 0.687 (95% confidence interval (CI): [0.687-0.688]) and 0.688 (95% confidence interval (CI): [0.687-0.689]) for the logistic regression (LR) with all variables, the gradient boosted decision tree (GBDT) with important variables and the GBDT with all variables respectively.
Conclusions:
The GBDT model with important predictors partly overestimated patients whose length of stay was less than 3 days and underestimated patients whose length of stay was longer than 3 days. But the better prediction performance of GBDT facilitates early intervention of ICU patients with a long period of hospitalization.
Citation
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