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This is super interesting work! I have two questions regarding the interpretation of learned representations.
In Section 3.5 - Interpreting code representations, the top k medical codes from each embedding dimension are selected to check if they are clinically related. However, by using skip-gram, according to this post, I think we should use cosine similarity to group medical codes but not the magnitudes of the values on specific embedding dimension. I think it is the angle between different medical codes that matter, not the magnitudes of the values on specific dimensions.
I have a similar question regarding interpreting visit representations. Specifically, why is it meaningful to compare the magnitudes of a specific dimension in the visit embedding space?
Thank you very much!
The text was updated successfully, but these errors were encountered:
Hello Ed @mp2893,
This is super interesting work! I have two questions regarding the interpretation of learned representations.
Thank you very much!
The text was updated successfully, but these errors were encountered: