Custom POS tagger for health issues
A few comments:

The term &quot;POS&quot; (Part Of Speech) has the specific meaning of &quot;grammatical category&quot; so it's not really what you mean here, you should probably say &quot;custom entities tags/categories/labels&quot;.
Your plan makes sense, but be aware that that you're addressing a really difficult problem so even with a really good dataset and method it's not going to work perfectly (probably far from it).
You're probably going to need a very large amount of annotated data for this to work, because there are many different ways to express a medical problem so the model will need many examples to extract the relevant patterns.
The standard way to address this kind of problem is a bit different when there are terminology  resources for the language: the medical terms can be annotated directly simply by pattern matching. If you have such resources and they are complete enough, this saves you the need to manually annotate and train a custom model.
Doing a recommender system on top of that is a second problem, and I think you need to think carefully about the goal:

if it's meant to be used by non-experts, it wouldn't make sense to recommend research articles.
The vast majority of the research literature is published in English, so there would be an additional problem to address here.
In case you're considering the whole medical literature, you might have a complexity issue because it's huge (PubMed contains around 30 millions abstracts, and that's without considering the full articles in PMC)
What would be the basis for recommendation? Is it based only on matching the terms in the query with the documents?