Sparse multi-output Gaussian processes for online medical time series prediction
Sparse multi-output Gaussian processes for online medical time series prediction
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DOI：
10.1186/s12911-020-1069-4
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发表时间：
2020-07-08
影响因子：
3.5
通讯作者：
Engelhardt, Barbara E.
中科院分区：
文献类型：
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作者：
Cheng, Li-Fang;Dumitrascu, Bianca;Engelhardt, Barbara E.
对于医院患者的实时监测，使用临床协变量和实验室测试结果的所有可用信息对患者的健康状况进行高质量的推断对于成功的医疗干预和改善患者的预后至关重要。开发一个可以从观察性大规模电子健康记录（EHR）中学习并做出准确实时预测的计算框架是关键的一步。在这项工作中，我们开发和探索贝叶斯非参数模型的基础上，多输出高斯过程（GP）回归医院patient monitoring.Methods我们提出MedGP，一个统计框架，纳入24个临床协变量，并支持丰富的参考数据集，从所观察到的协变量之间的关系可以推断和利用高质量的推断病人的状态随着时间的推移。为了做到这一点，我们开发了一个高度结构化的稀疏GP内核，以实现数万个时间点的易处理计算，同时估计临床协变量，患者和患者观察周期之间的相关性。MedGP具有优于当前方法的许多益处，包括（i）不需要时间序列数据的对齐，（ii）量化预测中的置信区域，（iii）利用庞大且丰富的患者数据库，和（四）推断临床协变量之间可解释的关系。结果我们评估并比较MedGP对来自两个医疗数据的三个患者亚组进行在线预测的任务的结果8,043名患者。我们发现MedGP提高了在线预测超过基线和国家的最先进的方法，几乎所有的协变量在不同的疾病亚组和hospital.Conclusions MedGP框架是强大的，有效的估计时间依赖性稀疏和不规则采样的医疗时间序列数据在线预测。公开可用的代码位于。
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.