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March 3, 20260 citationsOpen Access

Developing and externally validating machine learning models to forecast short-term risk of ventilator-associated pneumonia

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APAlec PeltekianWLWan-Ting LiaoWLWan‐Ting Liao

Key Points

  • VAP onset can be predicted up to a week in advance, indicating the effectiveness of machine learning models.
  • The models demonstrated strong predictive performance across different hospital systems, showing generalizability despite practice variations.
  • Analyzing routinely collected ICU data utilized advanced machine learning techniques, focusing on appropriate feature labeling.
  • Poor feature overlap limited model performance, emphasizing the need for consistent data features across hospitals.

Abstract

Machine learning models trained on routinely collected ICU data with careful labeling can anticipate VAP onset up to a week in advance with strong predictive performance. Model performance generalized to data from an entirely different hospital system despite differences in practice and labeling patterns, but did not perform well when there was poor feature overlap. Future work should focus on real-time prospective evaluation.

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Cite This Study

Peltekian et al. (2026) studied this question.

synapsesocial.com/papers/69a75f01c6e9836116a2a167https://doi.org/10.64898/2026.01.28.26344858
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