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首页> 外文会议>Sensing and analysis technologies for biomedical and cognitive applications 2016 >Independent component analysis decomposition of hospital emergency department throughput measures
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Independent component analysis decomposition of hospital emergency department throughput measures

机译：医院急诊科通过量指标的独立成分分析分解

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We present a method adapted from medical sensor data analysis, viz. independent component analysis of electroencephalography data, to health system analysis. Timely and effective care in a hospital emergency department is measured by throughput measures such as median times patients spent before they were admitted as an inpatient, before they were sent home, before they were seen by a healthcare professional. We consider a set of five such measures collected at 3,086 hospitals distributed across the U.S. One model of the performance of an emergency department is that these correlated throughput measures are linear combinations of some underlying sources. The independent component analysis decomposition of the data set can thus be viewed as transforming a set of performance measures collected at a site to a collection of outputs of spatial filters applied to the whole multi-measure data. We compare the independent component sources with the output of the conventional principal component analysis to show that the independent components are more suitable for understanding the data sets through visualizations.
机译：我们提出了一种根据医学传感器数据分析改编的方法。脑电图数据的独立成分分析，以卫生系统分析。医院急诊科的及时有效的护理是通过吞吐量度量的，例如患者入院前，送回家之前，在医疗保健专业人员就诊之前花费的中位数。我们考虑了在美国3,086家医院中收集的五种此类衡量指标。急诊室绩效的一个模型是，这些相关的吞吐量衡量指标是某些潜在来源的线性组合。因此，可以将数据集的独立成分分析分解视为将在站点收集的一组性能度量转换为应用于整个多度量数据的空间过滤器输出的集合。我们将独立成分的来源与常规主成分分析的输出进行比较，以表明独立成分更适合通过可视化来理解数据集。

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