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Physics lego figure. Photo.

Anders Björkelund


Physics lego figure. Photo.

Machine learning compared with rule-in/rule-out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations


  • Anders Björkelund
  • Mattias Ohlsson
  • Jakob Lundager Forberg
  • Arash Mokhtari
  • Pontus Olsson de Capretz
  • Ulf Ekelund
  • Jonas Björk

Summary, in English

Computerized decision‐support tools may improve diagnosis of acute myocardial infarction (AMI) among patients presenting with chest pain at the emergency department (ED). The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high‐sensitivity cardiac troponin T (hs‐cTnT) concentrations with varying sampling times, age, and sex in order to rule in or out AMI.
In this register‐based, cross‐sectional diagnostic study conducted retrospectively based on 5695 chest pain patients at 2 hospitals in Sweden 2013–2014 we used 5‐fold cross‐validation 200 times in order to compare the performance of an artificial neural network (ANN) with European guideline‐recommended 0/1‐ and 0/3‐hour algorithms for hs‐cTnT and with logistic regression without interaction terms. Primary outcome was the size of the intermediate risk group where AMI could not be ruled in or out, while holding the sensitivity (rule‐out) and specificity (rule‐in) constant across models.
ANN and logistic regression had similar (95%) areas under the receiver operating characteristics curve. In patients (n = 4171) where the timing requirements (0/1 or 0/3 hour) for the sampling were met, using ANN led to a relative decrease of 9.2% (95% confidence interval 4.4% to 13.8%; from 24.5% to 22.2% of all tested patients) in the size of the intermediate group compared to the recommended algorithms. By contrast, using logistic regression did not substantially decrease the size of the intermediate group.
Machine learning algorithms allow for flexibility in sampling and have the potential to improve risk assessment among chest pain patients at the ED.


  • Computational Biology and Biological Physics
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • eSSENCE: The e-Science Collaboration
  • Less invasive cardiac surgery
  • NPWT technology
  • Thoracic Surgery
  • Medicine, Lund
  • EpiHealth: Epidemiology for Health
  • Emergency medicine
  • Surgery and public health
  • Division of Occupational and Environmental Medicine, Lund University

Publishing year





Journal of the American college of emergency physicians open





Document type

Journal article


John Wiley and Sons


  • Cardiac and Cardiovascular Systems
  • Computer Systems




  • AIR Lund - Artificially Intelligent use of Registers

Research group

  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • Less invasive cardiac surgery
  • NPWT technology
  • Emergency medicine
  • Surgery and public health


  • ISSN: 2688-1152