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Photo of Mattias Ohlsson

Mattias Ohlsson


Photo of Mattias Ohlsson

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths


  • Ellen Tolestam Heyman
  • Awais Ashfaq
  • Ardavan Khoshnood
  • Mattias Ohlsson
  • Ulf Ekelund
  • Lina Dahlén Holmqvist
  • Markus Lingman

Summary, in English

BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.

OBJECTIVES: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.

METHODS: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM).

RESULTS: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.

CONCLUSION: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.


  • Emergency medicine
  • eSSENCE: The e-Science Collaboration
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • EpiHealth: Epidemiology for Health

Publishing year







Journal of Emergency Medicine





Document type

Journal article




  • Cardiac and Cardiovascular Systems
  • Anesthesiology and Intensive Care


  • machine learning
  • artificial intelligence
  • emergency department
  • emergency medicine
  • end-of-life
  • palliative care
  • Maskininlärning
  • artificial intelligence
  • akutsjukvård
  • palliativ vård
  • livets slutskede




  • AIR Lund - Artificially Intelligent use of Registers
  • Resource Management in the Emergency Department by using Machine Learning

Research group

  • Emergency medicine
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)


  • ISSN: 0736-4679