Independent component analysis decomposition of hospital emergency department throughput measures

Qiang He; Henry Chu

doi:10.1117/12.2228735

19 May 2016 Independent component analysis decomposition of hospital emergency department throughput measures

Qiang He, Henry Chu

Proceedings Volume 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016; 98710P (2016) https://doi.org/10.1117/12.2228735
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

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.

Citation Download Citation

Qiang He and Henry Chu "Independent component analysis decomposition of hospital emergency department throughput measures", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710P (19 May 2016); https://doi.org/10.1117/12.2228735

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