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Prior electrocardio...
Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients
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- Nyström, Axel (författare)
- Lund University,Lunds universitet,Avdelningen för arbets- och miljömedicin,Institutionen för laboratoriemedicin,Medicinska fakulteten,EPI@LUND,Forskargrupper vid Lunds universitet,Beräkningsvetenskap för hälsa och miljö,Division of Occupational and Environmental Medicine, Lund University,Department of Laboratory Medicine,Faculty of Medicine,Lund University Research Groups,Computational Science for Health and Environment
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- Olsson de Capretz, Pontus (författare)
- Lund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Skåne University Hospital
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- Björkelund, Anders (författare)
- Lund University,Lunds universitet,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Beräkningsvetenskap för hälsa och miljö,Forskargrupper vid Lunds universitet,Centre for Environmental and Climate Science (CEC),Faculty of Science,Computational Science for Health and Environment,Lund University Research Groups
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- Lundager Forberg, Jakob (författare)
- Lund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Helsingborg Hospital
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- Ohlsson, Mattias, 1967- (författare)
- Halmstad University,Lunds universitet,Högskolan i Halmstad,Akademin för informationsteknologi,Lund University, Lund, Sweden,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Beräkningsvetenskap för hälsa och miljö,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas,Computational Science for Health and Environment
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- Björk, Jonas (författare)
- Lund University,Lunds universitet,Kirurgi och folkhälsa,Forskargrupper vid Lunds universitet,EPI@LUND,Surgery and public health,Lund University Research Groups,Skåne University Hospital
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- Ekelund, Ulf (författare)
- Lund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Skåne University Hospital
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(creator_code:org_t)
- Philadelphia, PA : Elsevier, 2024
- 2024
- Engelska.
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Ingår i: Journal of Electrocardiology. - Philadelphia, PA : Elsevier. - 0022-0736 .- 1532-8430. ; 82, s. 42-51
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Abstract
Ämnesord
Stäng
- At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice. © 2023 The Authors
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Chest pain
- Electrocardiograms
- Emergency department
- Machine learning
- Major adverse cardiac event
- Neural networks
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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