Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.
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| Title: | Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study. |
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| Authors: | Toprak B; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; University Center of Cardiovascular Science, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department for Population Health Innovation, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany., Solleder H; Cardio-CARE, Medizincampus Davos, Davos, Switzerland., Di Carluccio E; Cardio-CARE, Medizincampus Davos, Davos, Switzerland., Greenslade JH; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia., Parsonage WA; Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia., Schulz K; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA., Cullen L; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia; Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia., Apple FS; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA; Hennepin Healthcare Research Institute, Minneapolis, MN, USA., Ziegler A; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany; Cardio-CARE, Medizincampus Davos, Davos, Switzerland; School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa., Blankenberg S; Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; University Center of Cardiovascular Science, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department for Population Health Innovation, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Partner Sites Hamburg/Kiel/Luebeck, Hamburg, Germany. Electronic address: s.blankenberg@uke.de. |
| Corporate Authors: | Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) group |
| Source: | The Lancet. Digital health [Lancet Digit Health] 2024 Oct; Vol. 6 (10), pp. e729-e738. Date of Electronic Publication: 2024 Aug 29. |
| Publication Type: | Journal Article; Observational Study; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101751302 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2589-7500 (Electronic) Linking ISSN: 25897500 NLM ISO Abbreviation: Lancet Digit Health Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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