comparison between med2vec and gram about gram

[mp2893]

Med2vec and GRAM are quite different actually. Med2vec is an unsupervised representation learning method. GRAM is typically used for improving the performance of supervised classifiers.

And you can actually combine med2vec and GRAM. In GRAM, you can achieve better performance if you pre-train the basic embeddings. In the paper, I trained those basic embeddings using GloVe, but you can use med2vec (you can use any representation learning technique actually).

But you ask an interesting question. I actually asked the very same question myself.
How much can we rely on hand-engineered domain knowledge?
The correct answer is, of course, if we have infinite data, we don't need any hand-engineered features. But in reality, you cannot always collect sufficient data for some medical codes (e.g. rare disease). Then the best you can do is rely on expert knowledge. Honestly, what else can we do?

When I said "the degree of conformity of the code representations to the groupers does not neces-sarily indicate how well the code representations capture the hidden relationships", I was assuming we had enough data. If we had enough data, then is it really a good idea to use the grouper as a evaluation metric? I was simply pointing this out.

Hope this helps,
Ed

[2g-XzenG]

Hello Ed,

Thanks! It took me a while to understand this paper, your respond is very helpful!