Use of Machine Learning Algorithms in Sepsis


  • The predictive value of sepsis clinical scoring systems is controversial but needs further improvement.

  • Machine learning methods have recently emerged in the use of predicting disease identification, diagnosis and treatment.

  • There is a current lack of comprehensive understanding of machine learning models of predictive variables.


  • Various clinical scoring systems for sepsis exist in order to help clinicians assess for sepsis and predict poor outcomes, specifically mortality.

  • Unfortunately, the predictive value of most of these tools remain unsatisfactory.

  • Machine learning has good predictive performance and has been shown useful in various predictions of disease prevention, diagnosis, treatment and prognosis for readmission and death.

  • Recently 2 major publications have comprehensively analyzed existing data of machine learning systems in determining the diagnostic accuracy and mortality risk prediction in sepsis.


Early Sepsis Recognition – L.M. Fleuren et al Intensive Care Med (2020) 46:383–400

  • 24 publications with 130 machine learning models covering ICU, ED and hospital wards.

  • Retrospective studies with substantial heterogeneity.

  • AUROC was used as the main performance metric.

  • Number of features used within the models ranged from 2 to 49.

  • Individual machine learning models have a high predictability of sepsis onset over traditional scoring tools.

  • Well controlled studies are needed to validate this initial information.

  • Risk of death was 24-34% lower with continuous infusion vs intermittent.

Machine Learning for Prediction of Sepsis Related Mortality -Y Zhang et al BMC Medical Informatics and Decision Making. 2023;23:283.

  • 50 studies with 104 machine learning models, 125 modeling variables.

  • 1.9 million patients; 270,361 (14.02%) mortality rate, with 247,519 in hospital deaths.

  • 38 additional studies on the accuracy of SOFA and qSOFA in predicting short term mortality risk in sepsis patients.

  • Machine learning models demonstrated favorable accuracy in predicting sepsis mortality with in the hospital and up to 1 month post hospitalization.


  • An improved sepsis assessment tool would enable clinicians to promptly diagnose and formulate treatment decisions.

  • Machine learning models appear to possess improved useability in terms of sepsis identification as well as mortality prediction.

  • Selection of appropriate modeling variables is a key factor in improving their predictive accuracy.

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Erkan Hassan is the Co-Founder & Chief Clinical Officer of Sepsis Program Optimization where he designs & oversees the implementation of solutions to optimize sepsis programs.

To discuss your organization’s Barriers of Effective Sepsis Care, contact Erkan by phone (844) 4SEPSIS (844-473-7747), email (, or video chat.