A Knowledge-driven Generative Model for Multi-implication C hinese Medical Procedure Entity Normalization (2020)

Abstract: Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical procedure entity normalization. However, nonstandard Chinese expressions and combined procedures present challenges in our problem. The existing strategies relying on the discriminative model are poorly to cope with normalizing combined procedure mentions. We propose a sequence generative framework to directly generate all the corresponding medical procedure entities. we adopt two strategies: category-based constraint decoding and category-based model refining to avoid unrealistic results. The method is capable of linking entities when a mention contains multiple procedure concepts and our comprehensive experiments demonstrate that the proposed model can achieve remarkable improvements over existing baselines, particularly significant in the case of multi-implication Chinese medical procedures.

## introduction named entity normalization @xcite , which is also known as entity linking, is one of the fundamental tasks within natural language processing @xcite @xcite . medical entity normalization is a typical problem of nen in the medical domain, which aims at linking references or mentions of medical terminology to standard entities in a given medical knowledge base (kb) such as the international statistical classification of diseases and related health problems 9th revision . due to the nature of the domain, although different occupational or writing habits can result in stan- * contribution during internship at national laboratory of pattern recognition, institute of automation, cas dard entities having different literal expressions, the linked standard entities of a given medical mention should always be unique. thus, unlike nen as applied generally @xcite @xcite , in the medical domain, the main challenge is not ambiguity -the same entity mention may be linked to different concepts, it is variation -the same underlying concept can be linked by different entity mentions. however, different from the normalization task of the medical entity with simple nominal structure, such as disease @xcite or anatomical body @xcite , chinese medical procedure normalization have to face the challenge of multi-implication -a mention which contains multiple procedure concepts should link to multiple standard procedure entities in kb. to clarify, these linked entities are instances of concepts in kb and have no parentchild relationships on each other. figure 1 shows some examples of chinese medical combined procedure entity normalization. in case 1, the mention left of the dotted line implicates two procedure concepts and links to two different standard entities, where the word "颅神经 (cranial nerve)" is omitted from the mention. besides, features in the textual level are not detailed enough to identify the exact number of procedures in given mentions such as case 2, which is a tri-combined procedure but with only one "+" delimiter. hence how to identify the number of linking entities for a given mention is crucial for chinese medical procedure normalizing. previous studies which adopt the discriminative model to solve the problem of variation in medical entity normalization @xcite @xcite involve two basic steps: first, entity candidates are selected from all entities in kb through artificially designed rules @xcite or text similarity methods, overall, we make the following contributions in this paper. ## proposed method the main idea underlying this work is based on introducing a sequence generative framework to directly generate all linked standard entities at once, which can then normalize both "uni-implication" and "multi-implication" types of mentions in an end-to-end fashion. in this section, we introduce the proposed category-based constraint decoding and model-refining methods, which can avoid generating unrealistic results and adapt the test sets belonging to particular categories. as the basis of our work, we first introduce two definitions used in considering the excellent performance of an existing self-attention-based transformer @xcite , we implement our method based on this architecture. the input and output of encoder and decoder are all character-based sequences. ## experimental settings in this section, we describe the datasets used in our experiments, and the details of training and evaluation. ## results and analysis ## conclusions and future work in this paper, we propose a sequence generative learning framework with a category-based constraint decoding and model-refining mechanism for chinese medical procedure normalization. the proposed model can achieve the endto-end generation of all corresponding standard entities for all types of input mentions, especially for "multi-implication" mentions. the "generating and re-ranking" strategy is employed to integrate the proposed generative model with a discriminative similarity re-ranking method to further improve normalization performance. our comprehensive experimental results demonstrate that the proposed model significantly outperforms the baseline methods. furthermore, the proposed model can be applied to the normalization of both "uniimplication" and "multi-implication" chinese medical procedure mentions. notwithstanding, considering the complexity of the domain specificity and the scarcity of training data, this challenging task is far from being solved. in the future, we plan to focus on how to improve the performance of medical entity normalization when resources are limited.