lnu.sePublications
System disruptions
We are currently experiencing disruptions on the search portals due to high traffic. We are working to resolve the issue, you may temporarily encounter an error message.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Appraisal of Artificial Intelligence for fall prevention & fall risk assessment
Linnaeus University, Faculty of Technology, Department of Informatics.ORCID iD: 0000-0001-7048-8089
2022 (English)In: Proceedings of the 5th International Conference on Informatics & Data-Driven MedicineLyon, France, November 18 - 20, 2022 / [ed] Shakhovska N., Chretien S., Izonin I., Campos J., CEUR-WS , 2022, Vol. 3302, p. 21-34Conference paper, Published paper (Refereed)
Abstract [en]

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.

Place, publisher, year, edition, pages
CEUR-WS , 2022. Vol. 3302, p. 21-34
Series
CEUR Workshop Proceedings, E-ISSN 1613-0073 ; 3302
Keywords [en]
Accident prevention, Data mining, eHealth, Learning algorithms, Machine learning, ’current, Ehealth, Emerging technologies, Fall prevention, Fall risk, Fall risk assessment, Healthcare systems, Information and Communication Technologies, Machine-learning, Risks assessments, Risk assessment
National Category
Information Systems
Research subject
Health and Caring Sciences, Health Informatics
Identifiers
URN: urn:nbn:se:lnu:diva-122911Scopus ID: 2-s2.0-85144202164OAI: oai:DiVA.org:lnu-122911DiVA, id: diva2:1776895
Conference
5th International Conference on Informatics and Data-Driven Medicine, IDDM 2022, 18-20 November 2022
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2023-08-18Bibliographically approved

Open Access in DiVA

fulltext(940 kB)174 downloads
File information
File name FULLTEXT01.pdfFile size 940 kBChecksum SHA-512
274c54821e06d0310060ed28473a688c6e6bbd2554e2af1af42bef08d20beb7e73d156ad2a9b9cfc38d4dcd3e12eed92f2bac30b0316f3f90e20e650a4c1b1b9
Type fulltextMimetype application/pdf

Scopus

Authority records

Campos, Jaime

Search in DiVA

By author/editor
Campos, Jaime
By organisation
Department of Informatics
Information Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 174 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 705 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf