RT info:eu-repo/semantics/article
T1 Machine learning in medical emergencies: a systematic review and analysis
A1 Robles Mendo, Inés
A1 Marques, Gonçalo
A1 Torre Díez, Isabel de la
A1 López-Coronado Sánchez-Fortún, Miguel
A1 Martín Rodríguez, Francisco
K1 Machine learning
K1 Health emergencies
K1 Emergency medicine
K1 Mobile applications
K1 32 Ciencias Médicas
K1 33 Ciencias Tecnológicas
AB Despite the increasing demand for artifcial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on diferent platforms, and its implementations in healthcare emergencies. The methodology applied for the identifcation and selection of the scientifc studies and the diferent applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n=4, 20%) or medical services or emergency services (n=4, 20%). Only 2 were focused on m-health (n=2, 10%). On the other hand, 12 apps were chosen for full testing on dif ferent devices. These apps dealt with pre-hospital medical care (n=3, 25%) or clinical decision support (n=3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and ofers solutions to improve the efciency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.
PB Springer
SN 0148-5598
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/48585
UL https://uvadoc.uva.es/handle/10324/48585
LA eng
NO Journal of Medical Systems, 2021, vol. 45, n. 10
NO Producción Científica
DS UVaDOC
RD 19-abr-2024