Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning

Interhospital transport of critically ill children carries inherent risks, including unexpected respiratory and cardiovascular deterioration. Early warning of impending deterioration may allow physicians to avert a more serious issue. We developed and evaluated lightweight, explainable machine learning models to forecast adverse physiological events up to 15 minutes in advance using continuously streamed vital signs and clinical data. Models were trained and evaluated on 1,519 transports conducted by a specialist paediatric critical care team in London (2016–2021). Transformer-based models incorporating vital sign time-series and vector-embedded diagnoses outperformed simpler models, achieving AUROC scores of 0.851 for respiratory and 0.792 for cardiovascular deterioration. Model interpretability was provided using Integrated Gradients, revealing alignment with clinical reasoning. Designed for deployment on edge devices, these models offer real-time, interpretable risk predictions in resource-limited transport settings. These results demonstrate that real-time, explainable machine learning models can accurately predict deterioration during interhospital paediatric transport using routinely collected data, supporting their potential role in enhancing early clinical intervention.