Modelo de machine learning en el proceso de atención de pacientes en emergencias hospitalarias

This study focused on the development and implementation of a machine learning algorithm aimed at optimizing the flow of care in emergency units, specifically prioritizing the classification or triage stage. To validate the tool's performance, statistical analyses such as Student's t-test and ANOVA were applied, yielding significant values (t = 7.823; p < 0.001). These findings confirm that the model is capable of reducing care delays and refining patient categorization. With an accuracy of 0.98 and an F1-score of 0.94 in detecting complex clinical cases, the proposal exceeds the standards of previous studies, establishing itself as a viable solution for hospitals with high patient saturation.