Ivana Pepic1, Robert Feldt2, Lars Ljungström3,4, Richard Torkar2, Daniel Dalevi5, Hanna Maurin Söderholm6, Lars-Magnus Andersson3, Marina Axelson-Fisk7, Katarina Bohm8,9, Bengt Arne Sjöqvist1,10, Stefan Candefjord11,12. 1. Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden. 2. Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden. 3. Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden. 4. Region Västra Götaland, Skaraborg Hospital, Department of Infectious Diseases, Skövde, Sweden. 5. Aweria AB, Gothenburg, 411 18, Sweden. 6. PreHospen Centre for Prehospital Research, University of Borås, Borås, 50 190, Sweden. 7. Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, 412 96, Sweden. 8. Karolinska Institute, Department of Clinical Science and Education, South General Hospital, Stockholm, Sweden. 9. Department of Emergency medicine, South General Hospital, Stockholm, Sweden. 10. MedTech West, Sahlgrenska University Hospital, Gothenburg, 413 45, Sweden. 11. Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden. stefan.candefjord@chalmers.se. 12. MedTech West, Sahlgrenska University Hospital, Gothenburg, 413 45, Sweden. stefan.candefjord@chalmers.se.
Abstract
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.
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.
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