Provides tools for predicting ICU length of stay and assessing ICU efficiency. It is based on the methodologies proposed by Peres et al. (2022, 2023), which utilize data-driven approaches for modeling and validation, offering insights into ICU performance and patient outcomes. References: Peres et al. (2022)< https://pubmed.ncbi.nlm.nih.gov/35988701/>, Peres et al. (2023)< https://pubmed.ncbi.nlm.nih.gov/37922007/>. More information: < https://github.com/igor-peres/ICU-Length-of-Stay-Prediction>.