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The Treatment Score (cont.)
Processing/Feature Engineering
In order to "score" an area, I needed to transform my TEDS-D data so that each row represents one CBSA area and I needed to be able to count the total number of clients for the different labels in each column for that area.
To do this, I one-hot-encoded each column, grouped the data by the CBSA codes, then summed the resulting columns. This then allowed me to treat each label in the original dataset as a variable to use in my score functions.
The Long List of Variables
The table below describes all of the variables I am using to generate this score.
I included the sign of each coefficient to distinguish between "good" and "bad" variables.
"Good" variables have a positive impact on the score.
"Bad" variables have a negative impact on the score.
To shorten some notation,
Some Score Functions
Some Score Plots
Discharge Scores (y) vs. Referral Scores (x)
Length-Of-Stay Scores (y) vs. Referral Scores (x)
Length-Of-Stay Scores (y) vs. Discharge Scores (x)
The Everything Scores
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