Autor según el artículo: Cuadrado, D; Valls, A; Riaño, D
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Valls Mateu, Aïda
Palabras clave: Machine learning Intensive care unit Days to discharge prediction Classification Artificial neural-networks machine learning intensive care unit icu classification admission
Resumen: Background: Accurate planning of the duration of stays at intensive care units is of utmost importance for resource planning. Currently, the discharge date used for resource management is calculated only at admission time and is called length of stay. However, the evolution of the treatment may be different from one patient to another, so a recalculation of the date of discharge should be performed, called days to discharge. The prediction of days to discharge during the stay at the ICU with statistical and data analysis methods has been poorly studied with low-quality results. This study aims to improve the prediction of the discharge date for any patient in intensive care units using artificial intelligence techniques. Methods: The paper proposes a hybrid method based on group-conditioned models obtained with machine learning techniques. Patients are grouped into three clusters based on an initial length of stay estimation. On each group (grouped by first days of stay), we calculate the group-conditioned length of stay value to know the predicted date of discharge, then, after a given number of days, another group-conditioned prediction model must be used to calculate the days to discharge in order to obtain a more accurate prediction of the number of remaining days. The study is performed with the eICU database, a public dataset of USA patients admitted to intensive care units between 2014 and 2015. Three machine learning methods (i.e., Random Forest, XGBoost, and lightGBM) are used to generate length of stay and days to discharge predictive models for each group. Results: Random Forest is the algorithm that obtains the best days to discharge predictors. The proposed hybrid method achieves a root mean square error (RMSE) and mean average error (MAE) below one day on the eICU dataset for the last six days of stay. Conclusions: Machine learning models improve quality of predictions for the days to discharge and length of stay for intensive care unit patients. The results demonstrate that the hybrid model, based on Random Forest, improves the accuracy for predicting length of stay at the start and days to discharge at the end of the intensive care unit stay. Implementing these prediction models may help in the accurate estimation of bed occupancy at intensive care units, thus improving the planning for these limited and critical health-care resources.
Áreas temáticas: Química Mathematics (miscellaneous) Mathematics (all) Mathematics General mathematics Engineering (miscellaneous) Computer science (miscellaneous) Astronomia / física
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: aida.valls@urv.cat
Identificador del autor: 0000-0003-3616-7809
Fecha de alta del registro: 2024-01-13
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Referencia al articulo segun fuente origial: Mathematics. 11 (23):
Referencia de l'ítem segons les normes APA: Cuadrado, D; Valls, A; Riaño, D (2023). Predicting Intensive Care Unit Patients’ Discharge Date with a Hybrid Machine Learning Model That Combines Length of Stay and Days to Discharge. Mathematics, 11(23), -. DOI: 10.3390/math11234773
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2023
Tipo de publicación: Journal Publications
Repositori URV