We present an end-to-end model using streaming physiological time series to accurately predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Our proposed model makes inference on both hypoxemia outcomes and future input sequences, enabled by a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learns future-indicative latent representation. All decoders share a memory-based encoder that helps capture the global dynamics of patient data. In a large surgical cohort of 73,536 surgeries at a major academic medical center, our model outperforms all baselines and gives a large performance gain over the state-of-the-art hypoxemia prediction system. With a high sensitivity cutoff at 80%, it presents 99.36% precision in predicting hypoxemia and 86.81% precision in predicting the much more severe and rare hypoxemic condition, persistent hypoxemia. With exceptionally low rate of false alarms, our proposed model is promising in improving clinical decision making and easing burden on the health system.

翻译：我们提出一个端到端模型,使用流动生理时间序列来准确预测已知在外科手术期间对病人造成严重伤害的罕见但危及生命的缺氧性贫血症的近期风险。我们提出的模型对低氧性贫血结果和未来输入序列进行推论,由同时优化标签预测歧视解码器的联合序列自动编码器和经过数据重建和预测培训的2个辅助解码器提供,它们无缝地学习未来指数潜在代表。所有解码器都有一个基于记忆的编码器,有助于捕捉全球病人数据动态。在一个主要学术医疗中心的73 536个手术组中,我们的模型超越了所有基线,并给最新水平的缺氧性贫血预测系统带来很大的性能收益。由于敏感度高达80%,我们提议的模型在预测低得多的缺氧性贫血症和预测中提供了99.36%的精确度,86.81%的精确度在预测更严重和罕见的缺氧病症、持续缺血症和持续缺血症方面,在错误的警报率极低的情况下,在改进临床决定和减轻负担方面很有希望。