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Machine learning fo...
Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation
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- de Capretz, Pontus Olsson (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,Beräkningsbiologi och biologisk fysik - Har omorganiserats,Institutionen för astronomi och teoretisk fysik - Har omorganiserats,Beräkningsvetenskap för hälsa och miljö,Forskargrupper vid Lunds universitet,Centre for Environmental and Climate Science (CEC),Faculty of Science,Computational Biology and Biological Physics - Has been reorganised,Department of Astronomy and Theoretical Physics - Has been reorganised,Computational Science for Health and Environment,Lund University Research Groups
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- Björk, Jonas (författare)
- Lund University,Lunds universitet,EPI@LUND,Forskargrupper vid Lunds universitet,Lund University Research Groups,Skåne University Hospital
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- Ohlsson, Mattias, 1967- (författare)
- Halmstad University,Lunds universitet,Högskolan i Halmstad,Akademin för informationsteknologi,Lund University, Lund, Sweden,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Astrofysik,Fysiska institutionen,Institutioner vid LTH,Lunds Tekniska Högskola,Beräkningsvetenskap för hälsa och miljö,Forskargrupper vid Lunds universitet,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas,Astrophysics,Department of Physics,Departments at LTH,Faculty of Engineering, LTH,Computational Science for Health and Environment,Lund University Research Groups
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- Mokhtari, Arash (författare)
- Lund University,Lunds universitet,NPWT teknologin,Forskargrupper vid Lunds universitet,Skonsammare hjärtkirurgi,NPWT technology,Lund University Research Groups,Less invasive cardiac surgery,Skåne University Hospital
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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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- 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)
- 2023-02-02
- 2023
- Engelska.
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Ingår i: BMC Medical Informatics and Decision Making. - London : BioMed Central (BMC). - 1472-6947. ; 23:1, s. 1-10
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Aims: In the present study, we aimed to evaluate the performance of machine learning (ML) models for identification of acute myocardial infarction (AMI) or death within 30 days among emergency department (ED) chest pain patients. Methods and results: Using data from 9519 consecutive ED chest pain patients, we created ML models based on logistic regression or artificial neural networks. Model inputs included sex, age, ECG and the first blood tests at patient presentation: High sensitivity TnT (hs-cTnT), glucose, creatinine, and hemoglobin. For a safe rule-out, the models were adapted to achieve a sensitivity > 99% and a negative predictive value (NPV) > 99.5% for 30-day AMI/death. For rule-in, we set the models to achieve a specificity > 90% and a positive predictive value (PPV) of > 70%. The models were also compared with the 0 h arm of the European Society of Cardiology algorithm (ESC 0 h); An initial hs-cTnT < 5 ng/L for rule-out and ≥ 52 ng/L for rule-in. A convolutional neural network was the best model and identified 55% of the patients for rule-out and 5.3% for rule-in, while maintaining the required sensitivity, specificity, NPV and PPV levels. ESC 0 h failed to reach these performance levels. Discussion: An ML model based on age, sex, ECG and blood tests at ED arrival can identify six out of ten chest pain patients for safe early rule-out or rule-in with no need for serial blood tests. Future studies should attempt to improve these ML models further, e.g. by including additional input data. © 2023, The Author(s).
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Acute myocardial infarction
- Chest pain
- Deep learning
- Emergency department
- High-sensitivity troponin
- Machine learning
- Acute myocardial infarction
- Chest pain
- Deep learning
- Emergency department
- High-sensitivity troponin
- Machine learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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