Stroke mortality prediction using machine learning: systematic review

  • Lihi Schwartz
    Corresponding author at: Derech Sheba 2, Ramat Gan, Israel, 5262000.
    Sheba Medical Center, Tel Hashomer, Israel, and Ben-Gurion University of the Negev, Be'er Sheva, Israel
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  • Roi Anteby
    Department of Surgery and Transplantation B, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
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  • Eyal Klang
    Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Mount Sinai, New York, NY, United States of America

    Deep Vision Lab, The Chaim Sheba Medical Center, Ramat Gan, Israel.

    Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
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  • Shelly Soffer
    Deep Vision Lab, The Chaim Sheba Medical Center, Ramat Gan, Israel.

    Internal Medicine B, Assuta Medical Center, Ashdod, Israel, and Ben-Gurion University of the Negev, Be'er Sheva, Israel
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Published:December 20, 2022DOI:


      • Data available at the time of admission may aid in stroke mortality prediction.
      • Machine learning has achieved great performance for stroke mortality prediction.
      • Age, high BMI and high NIHSS score are the most important predictors for mortality.
      • Deep learning has the potential to play an emerging role in stroke prognostication.


      Background and aims

      Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning–based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality.

      Materials and methods

      We searched MEDLINE/PubMed and Web of Science databases for original publications on machine learning applications in stroke mortality prediction, published between January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated using the tailored QUADAS-2 tool.


      Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction (0.67–0.98). In most of the articles, the models were applied for short-term post stroke mortality. The number of explanatory features used in the models to predict mortality ranged from 5 to 200, with substantial overlap in the variables included. Age, high BMI and high NIHSS score were identified as important predictors for mortality. Almost all studies had a high risk of bias in at least one category and concerns regarding applicability.


      Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.


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