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Length of stay (LoS) in the intensive care unit (ICU) is a common outcome measure used as an indicator of both quality of care and resource use. However, the existing analysis methods of LoS are poorly interpretable and extensible, and there is controversial for the predictive performance of LoS. In this paper, the study includes data from 1,214 unplanned ICU admissions to participate in the ICU of Sichuan Provincial People’s Hospital between Dec. 11, 2015 and Dec. 6, 2018. On the basis of these data, this study creates a highly accurate and predictive model using advanced preprocessing techniques, exploratory data analysis (EDA) and least absolute shrinkage and selection operator (LASSO) algorithm. Next, this study evaluates the predictive performance of the proposed model by 10-fold cross validation and external validation method using the root mean square prediction error (RMSPE), mean absolute error (MAE), and coefficient of determination ( $R^{2}$ ). The predictive performance of the proposed model is 0.88±0.13 day for RMSPE, 0.87±0.07 day for MAE and 0.35±0.09 for  $R^{2}$ . Experimental results show that the performance of the proposed method are competitive with the state-of-the-art methods and results. Furthermore, this study explores the risk factors for ICU LoS in survivors and non-survivors and compare their predictive performance.

Prediction of Length of Stay on the Intensive Care Unit Based on Least Absolute Shrinkage and Selection Operator