论文标题
一种用于改善机械通风ICU患者运动康复的贝叶斯分层模型
A Bayesian hierarchical model for improving exercise rehabilitation in mechanically ventilated ICU patients
论文作者
论文摘要
在重症监护室（ICU）机械通风的患者参加运动，这是他们康复的组成部分，以改善重症疾病对其身体机能的长期影响。但是，由于缺乏一种科学的方法来实时量化单个患者的运动强度水平，因此有效地实施了这些程序，这导致了广泛的一定大小的康复方法和次优的患者结果。在这项工作中，我们开发了一个具有时间相关的潜在高斯过程的贝叶斯分层模型，以预测$ \ dot vo_2 $，这是一种使用随时可用的生理数据的生理测量运动强度的度量。使用集成的嵌套拉普拉斯近似进行了推理。为了实际使用临床医生$ \ dot vo_2 $被归类为运动强度类别。基于这些分类进行了使用一位患者交叉验证的内部验证，并研究了描述分类不确定性的概率陈述的作用。
Patients who are mechanically ventilated in the intensive care unit (ICU) participate in exercise as a component of their rehabilitation to ameliorate the long-term impact of critical illness on their physical function. The effective implementation of these programmes is hindered, however, by the lack of a scientific method for quantifying an individual patient's exercise intensity level in real time, which results in a broad one-size-fits-all approach to rehabilitation and sub-optimal patient outcomes. In this work we have developed a Bayesian hierarchical model with temporally correlated latent Gaussian processes to predict $\dot VO_2$, a physiological measure of exercise intensity, using readily available physiological data. Inference was performed using Integrated Nested Laplace Approximation. For practical use by clinicians $\dot VO_2$ was classified into exercise intensity categories. Internal validation using leave-one-patient-out cross-validation was conducted based on these classifications, and the role of probabilistic statements describing the classification uncertainty was investigated.
