混合推理序列自编码器网络预测术中低氧血症
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks
摘要：
我们提出了一个端到端的模型，使用流式生理时间序列来预测低氧血症的近期风险，低氧血症是一种罕见但危及生命的疾病，已知会在手术过程中造成严重的患者伤害。受低氧血症事件是基于未来的低SpO2序列（即，血氧饱和度）的情况下，我们提出了混合推理网络（hiNet），使混合推理的未来低SpO2的情况和低氧血症的结果。hiNet集成了1）一个联合序列自动编码器，它同时优化了一个用于标签预测的判别解码器，以及2）两个用于数据重建和预测的辅助解码器，它们无缝地学习捕获从当前状态到未来状态的过渡的上下文潜在表示。所有解码器共享一个基于存储器的编码器，有助于捕获患者测量的全局动态。对于一个大型学术医疗中心的72，081例手术的大型手术队列，我们的模型优于强大的基线，包括最先进的低氧血症预测系统所使用的模型。hiNet能够以临床可接受的报警率实时预测近期低氧血症，因此有望改善临床决策并减轻围手术期护理负担。
英文摘要：
We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Inspired by the fact that a hypoxemia event is defined based on a future sequence of low SpO2 (i.e., blood oxygen saturation) instances, we propose the hybrid inference network (hiNet) that makes hybrid inference on both future low SpO2 instances and hypoxemia outcomes. hiNet integrates 1) a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and 2) two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learn contextual latent representations that capture the transition from present states to future states. All decoders share a memory-based encoder that helps capture the global dynamics of patient measurement. For a large surgical cohort of 72,081 surgeries at a major academic medical center, our model outperforms strong baselines including the model used by the state-of-the-art hypoxemia prediction system. With its capability to make real-time predictions of near-term hypoxemic at clinically acceptable alarm rates, hiNet shows promise in improving clinical decision making and easing burden of perioperative care.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
