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Machine Learning in Medical Emergencies: a Systematic Review and Analysis.

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Title: Machine Learning in Medical Emergencies: a Systematic Review and Analysis.
Authors: Mendo, Inés Robles1, Marques, Gonçalo1,2, de la Torre Díez, Isabel1 isator@tel.uva.es, López-Coronado, Miguel1, Martín-Rodríguez, Francisco3
Source: Journal of Medical Systems. Oct2021, Vol. 45 Issue 10, p1-16. 16p. 3 Diagrams, 7 Charts, 6 Graphs.
Subjects: Online information services, Health facilities, Hospital emergency services, Information storage & retrieval systems, Medical databases, Systematic reviews, Mobile apps, Artificial intelligence, Medical emergencies, Decision support systems, Decision making, MEDLINE, Medical research
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Machine Learning in Medical Emergencies: a Systematic Review and Analysis.
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  Data: <searchLink fieldCode="AR" term="%22Mendo%2C+Inés+Robles%22">Mendo, Inés Robles</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Marques%2C+Gonçalo%22">Marques, Gonçalo</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22de+la+Torre+Díez%2C+Isabel%22">de la Torre Díez, Isabel</searchLink><relatesTo>1</relatesTo><i> isator@tel.uva.es</i><br /><searchLink fieldCode="AR" term="%22López-Coronado%2C+Miguel%22">López-Coronado, Miguel</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Martín-Rodríguez%2C+Francisco%22">Martín-Rodríguez, Francisco</searchLink><relatesTo>3</relatesTo>
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10916-021-01762-3
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    Subjects:
      – SubjectFull: Online information services
        Type: general
      – SubjectFull: Health facilities
        Type: general
      – SubjectFull: Hospital emergency services
        Type: general
      – SubjectFull: Information storage & retrieval systems
        Type: general
      – SubjectFull: Medical databases
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      – SubjectFull: Systematic reviews
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      – SubjectFull: Mobile apps
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Medical emergencies
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      – SubjectFull: Medical research
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              Text: Oct2021
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