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Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis.

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Machine learning models show promise in predicting stroke-associated pneumonia (SAP), a common complication. These models can aid early identification of high-risk patients, but require further validation for clinical use.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Prediction Models

Background:

  • Stroke-associated pneumonia (SAP) is a significant post-stroke complication.
  • Machine learning (ML) models are increasingly developed for SAP prediction.

Purpose of the Study:

  • To systematically evaluate the predictive performance of ML, deep learning (DL), and neural network (NN) models for SAP.
  • To provide pooled performance metrics for these predictive models.

Main Methods:

  • Systematic literature search of PubMed, Embase, Scopus, and Web of Science.
  • Meta-analysis of 27 studies using R to calculate pooled AUC, accuracy, sensitivity, specificity, and DOR.
  • Analysis of model types (ML, DL, NN) and input data (clinical, imaging, etc.).

Main Results:

  • Pooled AUC of 0.84 and pooled accuracy of 0.80 indicate strong predictive performance.
  • Pooled sensitivity was 0.73 and specificity was 0.85.
  • ML models, primarily using clinical data, showed promising results without significant differences between ischemic and hemorrhagic stroke subgroups.

Conclusions:

  • ML-based models demonstrate significant potential for early SAP risk identification in clinical practice.
  • Further external validation and integration into clinical workflows are necessary for widespread adoption.