稀疏多输出高斯过程在线医学时间序列预测。
Sparse multi-output Gaussian processes for online medical time series prediction.
机构信息
Department of Electrical Engineering, Princeton University, Princeton, USA.
Lewis-Sigler Institute, Princeton University, Princeton, NJ, USA.
出版信息
BMC Med Inform Decis Mak. 2020 Jul 8;20(1):152. doi: 10.1186/s12911-020-1069-4.
BACKGROUND
For real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab test results is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on multi-output Gaussian process (GP) regression for hospital patient monitoring.
METHODS
We propose MedGP, a statistical framework that incorporates 24 clinical covariates and supports a rich reference data set from which relationships between observed covariates may be inferred and exploited for high-quality inference of patient state over time. To do this, we develop a highly structured sparse GP kernel to enable tractable computation over tens of thousands of time points while estimating correlations among clinical covariates, patients, and periodicity in patient observations. MedGP has a number of benefits over current methods, including (i) not requiring an alignment of the time series data, (ii) quantifying confidence regions in the predictions, (iii) exploiting a vast and rich database of patients, and (iv) inferring interpretable relationships among clinical covariates.
RESULTS
We evaluate and compare results from MedGP on the task of online prediction for three patient subgroups from two medical data sets across 8,043 patients. We find MedGP improves online prediction over baseline and state-of-the-art methods for nearly all covariates across different disease subgroups and hospitals.
CONCLUSIONS
The MedGP framework is robust and efficient in estimating the temporal dependencies from sparse and irregularly sampled medical time series data for online prediction. The publicly available code is at https://github.com/bee-hive/MedGP .
背景
为了实时监测医院患者，使用来自临床协变量和实验室测试结果的所有信息对患者的健康状况进行高质量推断，对于成功的医疗干预和改善患者预后至关重要。开发一个能够从观察性大规模电子健康记录（EHR）中学习并进行准确实时预测的计算框架是至关重要的一步。在这项工作中，我们开发并探索了一种基于多输出高斯过程（GP）回归的贝叶斯非参数模型，用于医院患者监测。
方法
我们提出了 MedGP，这是一种统计框架，它包含 24 个临床协变量，并支持丰富的参考数据集，从中可以推断出观察协变量之间的关系，并利用这些关系对患者状态进行高质量的实时推断。为此，我们开发了一种高度结构化的稀疏 GP 核，以实现对数千个时间点的可处理计算，同时估计临床协变量、患者之间的相关性以及患者观察中的周期性。MedGP 相对于当前方法具有许多优势，包括（i）不需要对时间序列数据进行对齐，（ii）量化预测的置信区间，（iii）利用庞大而丰富的患者数据库，以及（iv）推断临床协变量之间的可解释关系。
结果
我们在来自两个医疗数据集的三个患者亚组的在线预测任务上评估和比较了 MedGP 的结果，涉及 8043 名患者。我们发现，MedGP 提高了在线预测的性能，优于基线和最先进的方法，适用于不同疾病亚组和医院的几乎所有协变量。
结论
MedGP 框架在估计稀疏和不规则采样的医疗时间序列数据的时间依赖性方面具有鲁棒性和效率，可用于在线预测。可公开访问的代码位于 https://github.com/bee-hive/MedGP。
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