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Larsen Ch. 19 (Reason Codes (. To address the individual data points, each…
Larsen Ch. 19
Reason Codes
. To address the individual data points, each patient’s feature values need to be examined along with an analysis of how these values determined that patient’s probability of readmission
The left and right thresholds allow for the specification of the probability cutoffs to be used in this view
The bottom preview section of the screen shows the top three cases (those with the highest probabilities of readmission) and bottom three cases (those with the lowest probability of readmission).
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The Power of Language
head to the Insights screen again and select Text Mining. Once on this screen, use the orange down arrow to select the model diag_3_desc
At this point, access to a subject matter expert (SME) will be important during evaluation of the text model(s). For example, an SME may need to examine the diagnosis codes that contain the word “valve.”
A word cloud represents the words that have the highest coefficients The intensity of the red or blue colors indicates the size of their coefficient. By hovering over a term (one or more words in order from the text feature), the coefficient of that specific term is shown
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Hotspots
navigate first to Insights and then Hotspots. This visualization uses the RuleFit Classifier, which is an immediate red flag, considering that this is one of the algorithms that was stalled at 16% of the data during model creation
The Hotspot screen shows the most relevant (up to four) combinations of features and their effect on the target.60 Think of this diagram as a set of Venn diagrams where the largest and most overlapping hotspots are organized in the middle. Just as for the word cloud, the deeper the tone of the blue and the red, the more of an impact that particular combination of features that specifies a sub-group of patients has on the target
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