A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization

TL;DR

This paper introduces a multi-perspective combined recall and rank framework for Chinese procedure terminology normalization, improving accuracy and efficiency in mapping clinical mentions to knowledge base terminologies.

Contribution

It proposes a novel framework combining candidate generation, keyword-focused ranking, and fusion to handle multi-implication cases in Chinese medical terminology normalization.

Findings

Significant performance improvement over baseline methods

Enhanced handling of multi-implication terminology cases

Improved efficiency in terminology normalization tasks

Abstract

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.