  Medical terminology normalization aims to map the clinical mention to
terminologies come from a knowledge base, which plays an important role in
analyzing Electronic Health Record(EHR) and many downstream tasks. In this
paper, we focus on Chinese procedure terminology normalization. The expression
of terminologies are various and one medical mention may be linked to multiple
terminologies. Previous study explores some methods such as multi-class
classification or learning to rank(LTR) to sort the terminologies by literature
and semantic information. However, these information is inadequate to find the
right terminologies, particularly in multi-implication cases. In this work, we
propose a combined recall and rank framework to solve the above problems. This
framework is composed of a multi-task candidate generator(MTCG), a keywords
attentive ranker(KAR) and a fusion block(FB). MTCG is utilized to predict the
mention implication number and recall candidates with semantic similarity. KAR
is based on Bert with a keywords attentive mechanism which focuses on keywords
such as procedure sites and procedure types. FB merges the similarity come from
MTCG and KAR to sort the terminologies from different perspectives. Detailed
experimental analysis shows our proposed framework has a remarkable improvement
on both performance and efficiency.
