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

by Lundberg, Scott M; Nair, Bala; Vavilala, Monica S; Horibe, Mayumi; Eisses, Michael J; Adams, Trevor; Liston, David E; Low, Daniel King-Wai; Newman, Shu-Fang; Kim, Jerry; Lee, Su-In (2018)


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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AI and Medicine

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