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Early detection of ...
Early detection of sepsis using artificial intelligence : a scoping review protocol
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- Pepic, I. (författare)
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden,Chalmers tekniska högskola,Chalmers University of Technology
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- Feldt, Robert, 1972 (författare)
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden,Chalmers tekniska högskola,Chalmers University of Technology
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- Ljungström, Lars R. (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för infektionssjukdomar,Institute of Biomedicine, Department of Infectious Medicine
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- Torkar, Richard, 1971 (författare)
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden,Göteborgs universitet,University of Gothenburg
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- Dalevi, D. (författare)
- Aweria AB, Gothenburg, 411 18, Sweden
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- Maurin Söderholm, Hanna (författare)
- Högskolan i Borås,Akademin för vård, arbetsliv och välfärd,PreHospen,University of Borås
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- Andersson, Lars-Magnus, 1968 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för infektionssjukdomar,Institute of Biomedicine, Department of Infectious Medicine
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- Axelson-Fisk, Marina, 1972 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences
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- Bohm, K. (författare)
- Karolinska Institutet
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- Sjöqvist, Bengt-Arne, 1952 (författare)
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Chalmers tekniska högskola,Chalmers University of Technology
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- Candefjord, Stefan, 1981 (författare)
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2021-01-16
- 2021
- Engelska.
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Ingår i: Systematic Reviews. - : Springer Nature. - 2046-4053. ; 10:1
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence.Methods: The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.Ethics and dissemination: The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Omvårdnad (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Nursing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Health Care Service and Management, Health Policy and Services and Health Economy (hsv//eng)
- SAMHÄLLSVETENSKAP -- Medie- och kommunikationsvetenskap -- Biblioteks- och informationsvetenskap (hsv//swe)
- SOCIAL SCIENCES -- Media and Communications -- Information Studies (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Medicinsk etik (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Medical Ethics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Allmänmedicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- General Practice (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Infektionsmedicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Infectious Medicine (hsv//eng)
Nyckelord
- Artificial intelligence
- Clinical decision support
- Emergency department
- Machine learning
- Prehospital care
- Sepsis
- Article
- data extraction
- data processing
- decision support system
- diagnostic test accuracy study
- electronic medical record
- emergency care
- emergency ward
- human
- intensive care unit
- Internet
- mathematical model
- medical care
- practice guideline
- receiver operating characteristic
- sensitivity and specificity
- septic shock
- systematic review
- validation study
- Människan i vården
- The Human Perspective in Care
- Artificial intelligence
- Clinical decision support
- Emergency department
- Machine learning
- Prehospital care
- Sepsis
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Pepic, I.
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Feldt, Robert, 1 ...
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Ljungström, Lars ...
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Torkar, Richard, ...
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Dalevi, D.
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Maurin Söderholm ...
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Andersson, Lars- ...
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Axelson-Fisk, Ma ...
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Bohm, K.
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Sjöqvist, Bengt- ...
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Candefjord, Stef ...
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Hälsovetenskap
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och Omvårdnad
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Hälsovetenskap
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och Hälso och sjukvå ...
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- SAMHÄLLSVETENSKAP
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SAMHÄLLSVETENSKA ...
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och Medie och kommun ...
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och Biblioteks och i ...
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Hälsovetenskap
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och Medicinsk etik
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Allmänmedicin
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Infektionsmedici ...
- Artiklar i publikationen
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Systematic Revie ...
- Av lärosätet
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Högskolan i Borås
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Chalmers tekniska högskola
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Karolinska Institutet
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Göteborgs universitet