ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Deep Learning Method

Intensive care units (ICUs) serve patients with life-threatening conditions. However, the availability of ICU resources remains scarce worldwide. Therefore, it is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, the current ICU outcome prediction methods have limitations such as unsatisfactory accuracy and dependence on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based additive convolutional neural network (WT-A-CNN) that requires only patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. Furthermore, the additive structure of the model can be used for model interpretation and analyzing which input signal is more useful for ICU outcome prediction for different patient cohorts. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to act promptly on patients at risk and reduce the negative impacts on patient outcomes.