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Photo of Mattias Ohlsson

Mattias Ohlsson


Photo of Mattias Ohlsson

Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation


  • Pontus Olsson de Capretz
  • Anders Björkelund
  • Jonas Björk
  • Mattias Ohlsson
  • Arash Mokhtari
  • Axel Nyström
  • Ulf Ekelund

Summary, in English

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.


  • Emergency medicine
  • Astrophysics
  • eSSENCE: The e-Science Collaboration
  • EpiHealth: Epidemiology for Health
  • LU Profile Area: Natural and Artificial Cognition
  • NPWT technology
  • Less invasive cardiac surgery

Publishing year





BMC Medical Informatics and Decision Making





Document type

Journal article


BioMed Central (BMC)


  • Cardiac and Cardiovascular Systems


  • Acute myocardial infarction
  • Chest pain
  • Deep learning
  • Emergency department
  • High-sensitivity troponin
  • Machine learning




  • AIR Lund - Artificially Intelligent use of Registers

Research group

  • Emergency medicine
  • NPWT technology
  • Less invasive cardiac surgery


  • ISSN: 1472-6947