Statistical inference for continuous variables and critical illness monitoring

危重疾病的定义是急性器官衰竭的证据，需要监测和/或支持，无论是药物还是机器。密切监控不可避免地会产生来自多个来源的大量数据。这些数据被用于床边的临床决策。数据以不同的频率产生，例如心率和氧气水平等生命体征可以在床边连续监测。血气分析每4-6小时进行一次，其他血液检查每12-24小时进行一次。许多变量是相互关联的，治疗可能对某些变量产生可预测的影响。我们建议使用贝叶斯多级建模和贝叶斯网络，使用真实的患者数据（来自UCLH的bbbb5万名患者和来自大奥蒙德街医院的b> 5000名患者）来生成间歇性采样值的连续估计和缺失机制的模型。根据呼吸机、生命体征、药物使用和血液测试的数据，这些将用于模拟pH值的持续变化以及血红蛋白与氧气的结合。这项工作将成为UCL CHIMERA中心（www.ucl.ac.uk/chimera）的一部分，该中心使用多学科方法通过真实的患者数据来增强对人类生理学的理解。研究领域:1。统计学与应用概率论运筹学

Critical illness is defined by the evidence of acute organ failure needing monitoring and/or support, either with drugs or machines. Close monitoring inevitably generates large amounts of data from multiple sources. These data are used to make clinical decisions by the bedside. Data are generated at different frequencies e.g. vital signs such as heart rate and oxygen levels may be monitored continuously at the bedside. Blood gas analysis may be undertaken every 4-6 hours, other blood tests may be performed 12-24 hourly. Many of the variables are inter-connected and treatments may have predictable effects on some of the variables. We propose the use of Bayesian multi-level modelling and Bayesian networks using real patient data (>50000 patients from UCLH and >5000 patients from Great Ormond Street Hospital) to generate continuous estimations of intermittently sampled values and model for the missingness mechanism. These wil be applied to modelling the pH changes continuously and how haemoglobin binds to oxygen, based on data from the ventilator, vital signs, drug used and blood tests. The work will be part of the UCL CHIMERA hub (www.ucl.ac.uk/chimera) which uses a multi-disciplinary approach to enhance the understanding of human physiology using real patient data.Research Areas:1. Statistics and applied probability 2. Operational Research