Prediction of in-hospital death among patients admitted to a tertiary care hospital over the first 10 years: a machine learning approach.

Purpose: To describe the pre- and post-admission characteristics of hospitalized patients in a tertiary care hospital and to adjust machine learning models capable of predicting and identifying the factors that are associated with and have a greater prognostic value for in-hospital death. Materials and methods: This was a retrospective study based on data from patients who were discharged from a Mexican tertiary care hospital during its first 10 years of operation (2007-2016). Preadmission characteristics were analyzed using descriptive statistics. Comparison tests (Mann-Whitney Results: In total, 55,253 hospital discharges were considered, only 45,011 (0-101 years) had complete data, and the rate of in-hospital death was 4.17%. In total, 70% of the data were used for training and 30% for testing. Two-to-two comparisons between areas under the curve (AUCs) revealed that XGBoost (AUC = 0.9162) outperformed logistic regression (AUC = 0.9036) and random forest (AUC = 0.8978) ( Conclusions: Owing to its ability to capture complex patterns, the XGBoost model makes it possible to identify patients with a relatively high risk of in-hospital death using the data available at hospital admission. This constitutes a support tool for decision-making, helping to determine which patients require closer monitoring and follow-up during their hospital stay to improve the quality of medical care.