TY - GEN
T1 - Use of Machine Learning in Hospital Emergency Care for Patients
AU - Auqui José Antonio, Ogosi
AU - Gutiérrez Juan Enrique, Sigarrostegui
AU - Ángeles Patricia Noemí, Piscoya
AU - Sotomayor Daniel Alejandro, Yucra
AU - Abarca Julio Elmer, Sotomayor
AU - Azabache Iván Carlo, Petrlik
N1 - Publisher Copyright:
© 2024 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - hospital emergency
KW - Machine learning
KW - medical care
KW - process of care
UR - https://www.scopus.com/pages/publications/85203800974
U2 - 10.18687/LACCEI2024.1.1.1130
DO - 10.18687/LACCEI2024.1.1.1130
M3 - Conference contribution
AN - SCOPUS:85203800974
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - Proceedings of the 22nd LACCEI International Multi-Conference for Engineering, Education and Technology
T2 - 22nd LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2024
Y2 - 17 July 2024 through 19 July 2024
ER -