Development and validation of a machine learning model for predicting stroke-associated pneumonia in older patients with acute ischemic stroke - Takeaways - MDSpire

Development and validation of a machine learning model for predicting stroke-associated pneumonia in older patients with acute ischemic stroke

  • By

  • Wen-Jie Chu

  • Si-Ran Zhang

  • Qi-Lun Lai

  • Jing-Ying Yu

  • Yi-Qian Xu

  • June 10, 2026

  • 0 min

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  • 1

    The study developed a machine learning model to predict stroke-associated pneumonia (SAP) risk in elderly patients with acute ischemic stroke (AIS).

  • 2

    A total of 1,011 patients aged 65 and older were included, with an SAP incidence of 18.79% identified during the study period.

  • 3

    The Support Vector Machine (SVM) model achieved an accuracy of 0.773 and an AUC of 0.794, demonstrating effective predictive performance for SAP.

  • 4

    LASSO regression identified 12 key predictive features, enhancing the model's ability to assess SAP risk using routine clinical data.

  • 5

    An online platform was created for clinical use, facilitating early identification and management of high-risk elderly patients for SAP.

Original Source(s)

Development and validation of a machine learning model for predicting stroke-associated pneumonia in older patients with acute ischemic stroke

  • by Wen-Jie Chu, Si-Ran Zhang, Qi-Lun Lai, Jing-Ying Yu, Yi-Qian Xu

  • June 10, 2026

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