A Deep Learning Method for Intraoperative Age-agnostic and Disease-specific Cardiac Output Monitoring from Arterial Blood Pressure

Cardiac output monitoring plays an important role in intraoperative or intensive care medicine. Arterial pressure waveform derived cardiac output monitoring has been mainly used for real clinical fields despite its inexact output because gold standard thermodilution based cardiac output monitoring is too invasive to use it widely. In this study, we propose DLAPCO, the novel deep learning method for the more accurate and age-agnostic arterial pressure waveform derived cardiac output monitoring. DLAPCO exploits two attention mechanism to calibrate the model’s output to medical procedure or overcome the locality of convolutional neural network in analyzing raw vital signs. Through the experiments using the real-world hospital intraoperative data, we have shown that DLAPCO significantly outperforms the commercial arterial pressure waveform derived cardiac output monitoring device which use demographic information.