Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
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DOI：
10.1038/s41551-018-0304-0
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发表时间：
2018-10-01
影响因子：
28.1
通讯作者：
Lee, Su-In
中科院分区：
文献类型：
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作者：
Lundberg, Scott M.;Nair, Bala;Lee, Su-In
虽然麻醉师努力避免术中低氧血症，但目前还不可能可靠地预测未来的术中低氧血症。在这里，我们报告了一个基于机器学习的系统的开发和测试，该系统可以预测低氧血症的风险，并在全身麻醉期间真实的时间内解释风险因素。该系统接受了来自50 000多例手术电子病历的每分钟数据的培训，通过提供可解释的低氧血症风险和影响因素，提高了麻醉师的绩效。对预测的解释与文献和麻醉师的先验知识大致一致。我们的研究结果表明，如果麻醉师目前预计15%的低氧血症事件，在该系统的帮助下，他们可以预计30%，其中很大一部分可能受益于早期干预，因为它们与可修改的因素。该系统可以通过提供对由患者或手术的某些特征引起的风险的确切变化的一般见解，来帮助提高对麻醉护理期间低氧血症风险的临床理解。
Although anaesthesiologists strive to avoid hypoxaemia during surgery, reliably predicting future intraoperative hypoxaemia is not possible at present. Here, we report the development and testing of a machine-learning-based system that predicts the risk of hypoxaemia and provides explanations of the risk factors in real time during general anaesthesia. The system, which was trained on minute-by-minute data from the electronic medical records of over 50,000 surgeries, improved the performance of anaesthesiologists by providing interpretable hypoxaemia 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 hypoxaemia events, with the assistance of this system they could anticipate 30%, 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 hypoxaemia risk during anaesthesia care by providing general insights into the exact changes in risk induced by certain characteristics of the patient or procedure.