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Data visualization using kernel density estimation to examine patterns of physician practice
Cerrito P.  Applied computing (Proceedings of the 2003 ACM symposium, Melbourne, Florida, Mar 9-12, 2003)275-279.2003.Type:Proceedings
Date Reviewed: Apr 22 2004

Graphical representations of probability density functions (PDFs) arising from the application of kernel density estimation can provide useful visualizations of data. This paper reports on the results of using kernel density estimation for several data sets concerned with physician practice. Visualizations show how one physician was far more consistent in administering heparin doses, and how patients at risk from infection received a greater number of different antibiotics.

The results of the visualizations are compared with the results from established statistical techniques, but in an unsystematic way: sample sizes and significance levels are sometimes not given, and only one bar graph is presented. A systematic comparison would have allowed the reader to better judge the utility of the visualizations. Even better would be a dedicated experiment, addressing the issue of whether or not graphical representations of PDFs provide greater insights than established statistical and visualization techniques.

In section 3.2, it is stated that analysis of variance requires a large separation between curves to obtain statistical significance. This seems to indicate a lack of understanding of the statistical power of a test, and of how it relates to sample size, and the size of the effect one is interested in measuring. Also, the paper does not address the variations in PDFs that can be obtained as a result of employing different methods to estimate the bandwidth for smoothing.

This paper will be of interest to those working in medical informatics and those developing data analysis tools.

Reviewer:  Andy Brooks Review #: CR129489 (0411-1412)
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