Explainable machine-learning predictions for the prevention of hypoxaemia during surgery.

ABSTRACT: Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time during general anaesthesia, predicts the risk of hypoxemia and provides explanations of the risk factors. The system, which was trained on minute-by-minute data from the electronic medical records of over fifty thousand surgeries, improved the performance of anaesthesiologists when providing interpretable hypoxemia risks and contributing factors. The explanations for the predictions are broadly consistent with the literature and with prior knowledge from anaesthesiologists. Our results suggest that if anaesthesiologists currently anticipate 15% of

SUBMITTER: Lundberg SM 

PROVIDER: S-EPMC6467492 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature