Machine Learning in Medical Emergencies: a Systematic Review and Analysis.
Despite the increasing demand for artificial 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 a...
| Published in: | Journal of Medical Systems Vol. 45; no. 10; pp. 1 - 17 |
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| Main Authors: | , , , , |
| Format: | algorithm research systematic review tables/charts Journal Article |
| Published: |
Springer Nature
Oct2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152790918&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152790918 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2021 vid: 45 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152790918 152790918 152790918 10.1007/s10916-021-01762-3 152790918 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning in Medical Emergencies: a Systematic Review and Analysis. aug: au: Mendo, Inés Robles Marques, Gonçalo de la Torre Díez, Isabel López-Coronado, Miguel Martín-Rodríguez, Francisco affil: Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47.011, Valladolid, Spain sug: subj: Health Facilities Emergencies Artificial Intelligence Utilization Research, Medical Human Systematic Review PubMed Emergency Service Decision Support Techniques Mobile Applications Decision Support Systems, Clinical ab: Despite the increasing demand for artificial 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 different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different 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 different 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 offers solutions to improve the efficiency 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. pubtype: Academic Journal doctype: algorithm research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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