Publications

Efficient patient monitoring on medical-surgical wards is crucial to prevent adverse events. Standard episodic inpatient assessment of vital signs can miss changes in health status and delay risk recognition. This study developed a wearable-based deep learning model using only 9 inputs to identify the onset of deterioration earlier than traditional early warning systems. The model could generalize to produce clinical alerts ahead of rapid response team (RRT) interventions, unplanned intensive care unit (ICU) transfers, intubations, cardiac arrests, and in-hospital deaths. Using multiple stages of validation on 888 adult non-ICU inpatient visits, the RNN model predicted both periods of elevated MEWS scores (ROC AUC 0.89 +/- 0.3, PR AUC 0.58 +/- 0.14) and adverse clinical outcomes (accuracy: 81.8%) up to an average of 17 hours in advance.