  Clinical concept extraction often begins with clinical Named Entity
Recognition (NER). Often trained on annotated clinical notes, clinical NER
models tend to struggle with tagging clinical entities in user queries because
of the structural differences between clinical notes and user queries. User
queries, unlike clinical notes, are often ungrammatical and incoherent. In many
cases, user queries are compounded of multiple clinical entities, without comma
or conjunction words separating them. By using as dataset a mixture of
annotated clinical notes and synthesized user queries, we adapt a clinical NER
model based on the BiLSTM-CRF architecture for tagging clinical entities in
user queries. Our contribution are the following: 1) We found that when trained
on a mixture of synthesized user queries and clinical notes, the NER model
performs better on both user queries and clinical notes. 2) We provide an
end-to-end and easy-to-implement framework for clinical concept extraction from
user queries.
