PMC4209908_01
PMC4209908_02
PMC4209908_03
Number of nodes in graph (Y-axis) for Case Study 1 filtered with different class count thresholds (X-axis)
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F4
Figure 4 shows the resulting graph sizes for varying class count thresholds. Figure 4 shows the exact filtering results based on class count thresholds. The original graph for this data set has over 6000 nodes, while Figure 4 shows the filtered graph sizes between 130 to 90 nodes. Figure 4, both figures have similar decreasing trends with increasing class count thresholds, however they do show different details due to the two different underlying data sets. Figure 6 looks much more linear comparing to Figure 4. Figure 4 and Figure 6 show important linear components, which support the methodology (linear model) we used to get filtered graphs. Figure 7 in Appendix shows the summary view of the data set for Case Study 2 according to subjects' class counts.
The circled area is particular for Case Study 1 data set (the most common ICD9-CM codes in BTRIS), showing these are the conditions associated with readmissions in 2011. Filtered graph from Case Study 1 with class count threshold of 2300 shown in Figure 5 in Appendix. Color annotations correspond to different class count ranges: dark orange, 20000-80000; orange, 14000-20000; green, 6000-10000; lime, 4000-6000; light green, 3000-4000; and grey, 2000-3000.
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Figure 5 in Appendix). Figure 5 in Appendix shows the summary view of the data set for Case Study 1 according to ICD9 codes' class counts. The circle area in Figure 5 highlights nodes that do not appear in the graph for Case Study 2 (below): malignant neoplasm of bone, connective tissue, skin & breast and secondary malignant neoplasm of lung.
Number of nodes in graph (Y-axis) for Case Study 2 filtered with different class count thresholds (subject counts, X-axis)
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F6
Figure 6 demonstrates the filtering details for the filtered graphs with the final nodes between 90 and 130 nodes, which correspond to different class count thresholds. Figure 4, both figures have similar decreasing trends with increasing class count thresholds, however they do show different details due to the two different underlying data sets. Figure 6 looks much more linear comparing to Figure 4. Figure 4 and Figure 6 show important linear components, which support the methodology (linear model) we used to get filtered graphs. Figure 7 in Appendix shows the summary view of the data set for Case Study 2 according to subjects' class counts.
The circled area is one of the particular areas for Case Study 2 data set (Case Study 2, the most patients coded with ICD9-CM codes in BTRIS 2011 with a class count threshold of 750): symptoms involving respiratory system and other chest symptoms. The whole filtered graph of Case Study 2 are shown in Figure 7 in Appendix. Color annotations correspond to different subject ranges: dark orange, 8000-10000; orange, 6000-8000; lime, 1200-2000; light green, 1000-1200; grey, 800-1000.
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F7
For example, a class count threshold of 750 results in a graph of 105 nodes (Figure 7 in Appendix). Figure 4 and Figure 6 show important linear components, which support the methodology (linear model) we used to get filtered graphs. Figure 7 in Appendix shows the summary view of the data set for Case Study 2 according to subjects' class counts.
PMC4209908_04