Prediction of Length-of-stay at Intensive Care Unit (ICU) Using Machine Learning based on MIMIC-III Database

The length-of-stay (LOS) is critical for patient care and accommodation in the intensive care unit (ICU). In this work, we developed a framework to predict the LOS using the Medical Information Mart for Intensive Care (MIMIC-III) database. We extracted six features from individual patients and submitted them to the regressors model and examined how well these features could predict LOS. We considered four prediction regimes; extreme gradient boosting (XGBoost), support vector regressor, random forest, and voting regressor. Our analysis reveals that XGBoost yields the best result among other regressors with R2 0.86 and root mean square error (RMSE) 1.2. Remarkably, our results show that ICD9 (9th International classification of diseases code), saline intake per hour, and drug rates are the top three critical features for predicting the LOS.