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

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