Enabling artificial intelligence in high acuity medical environments.
Acute patient treatment can heavily profit from AI-based assistive and decision support systems, in terms of improved patient outcome as well as increased efficiency. Yet, only very few applications have been reported because of the limited accessibility of device data due to the lack of adoption of...
| Publicado en: | Minimally Invasive Therapy & Allied Technologies Vol. 28; no. 2; pp. 120 - 127 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135992323&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135992323 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13645706 J4S jtl: Minimally Invasive Therapy & Allied Technologies issn: 13645706 maglogo: Y pubinfo: dt: Apr2019 vid: 28 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 135992323 135992323 135992323 10.1080/13645706.2019.1599957 135992323 ppf: 120 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Enabling artificial intelligence in high acuity medical environments. aug: au: Kasparick, Martin Andersen, Björn Franke, Stefan Rockstroh, Max Golatowski, Frank Timmermann, Dirk Ingenerf, Josef Neumuth, Thomas affil: Institute of Applied Microelectronics and Computer Engineering (IMD), University of Rostock, Rostock, Germany sug: subj: Artificial Intelligence Acute Care Technology, Medical Minimally Invasive Procedures Data Analytics Semantic Web Treatment Outcomes Systems Integration Decision Support Systems, Clinical Equipment and Supplies ab: Acute patient treatment can heavily profit from AI-based assistive and decision support systems, in terms of improved patient outcome as well as increased efficiency. Yet, only very few applications have been reported because of the limited accessibility of device data due to the lack of adoption of open standards, and the complexity of regulatory/approval requirements for AI-based systems. The fragmentation of data, still being stored in isolated silos, results in limited accessibility for AI in healthcare and machine learning is complicated by the loss of semantics in data conversions. We outline a reference model that addresses the requirements of innovative AI-based research systems as well as the clinical reality. The integration of networked medical devices and Clinical Repositories based on open standards, such as IEEE 11073 SDC and HL7 FHIR, will foster novel assistance and decision support. The reference model will make point-of-care device data available for AI-based approaches. Semantic interoperability between Clinical and Research Repositories will allow correlating patient data, device data, and the patient outcome. Thus, complete workflows in high acuity environments can be analysed. Open semantic interoperability will enable the improvement of patient outcome and the increase of efficiency on a large scale and across clinical applications. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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