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 hypoxemia events, with this system’s
assistance they would anticipate 30% of them, a large portion of which may
benefit from early intervention because they are associated with modifiable
factors. The system can help improve the clinical understanding of hypoxemia
risk during anaesthesia care by providing general insights into the exact
changes in risk induced by certain patient or procedure characteristics.
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