Modelo de machine learning en el proceso de atención de pacientes en emergencias hospitalarias
10.47422/jstri.v7i1.71
Acceso abiertoFuentes: histórico de OJS (PKP, por objeto) + tracker en la revista + 52 vista(s) en este índice. Citas: se consultan automáticamente en índices bibliométricos (Crossref y otros) y se muestra el mayor valor encontrado.
Impacto y citas
Acceso abierto: diamante Área: Health Professions ODS 3 · Good health and well-being
Temas: Artificial Intelligence in Healthcare · Emergency and Acute Care Studies · Machine Learning in Healthcare
Resumen
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.
Lecturas por mes y tendencia
Barras: vistas del resumen y lecturas de archivos (tracker + índice). Línea punteada: tendencia lineal y predicción de 3 meses (estimación estadística, no un dato).
Palabras clave
Machine LearningHospital TriageEmergency CareTriaje hospitalarioAtención de emergencias