Use of Machine Learning in Hospital Emergency Care for Patients (#1130)

Authors

Ogosi Auqui, José Antonio

Sigarrostegui Gutierrez, Juan Enrique

Piscoya Ángeles, Patricia Noemí

Yucra Sotomayor, Daniel Alejandro

Sotomayor Abarca, Julio Elmer

Petrlik Azabache, Iván Carlo

Abstract

This paper addresses the design and implementation of a Machine Learning model in the process of patient care in hospital emergencies. With the aim of improving efficiency and quality in the provision of emergency medical services, the application of advanced machine learning techniques is proposed. The central problem lies in optimizing the triage process and the assignment of priorities, crucial aspects in the emergency field. The research is framed within a descriptive and applied approach, using observation as the main data collection technique. The observation sheet, structured on the basis of specific indicators, serves as an instrument to evaluate the performance of the model in practical situations. The main objective of this approach is the effective integration of Machine Learning technology into the workflow of hospital emergency departments, with a view to improving decision-making, resource allocation and, ultimately, patient care.