Exploring Word Segmentation and Medical Concept Recognition for C hinese Medical Texts (2021)

Abstract: Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs. One challenge to these tasks is the lack of medical domain datasets with high-quality annotations, especially medical-related tags that reveal the characteristics of Chinese EMRs. In this paper, we collected a Chinese EMR corpus, namely, ACEMR, with human annotations for Chinese word segmentation and EMR-related tags. On the ACEMR corpus, we run well-known models (i.e., BiLSTM, BERT, and ZEN) and existing state-of-the-art systems (e.g., WMSeg and TwASP) for CWS and medical concept recognition. Experimental results demonstrate the necessity of building a dedicated medical dataset and show that models that leverage extra resources achieve the best performance for both tasks, which provides certain guidance for future studies on model selection in the medical domain.

## introduction medical language processing (mlp), i.e., natural language processing (nlp) for the electronic medical record (emr), has drawn significant attention over the past few decades @xcite @xcite . emr normally records the entire process of a patient's examination, diagnosis, and treatment by clinicians in the hospital, and contains a large amount of medical information, which, if extracted properly, can be used to train a machine learning model as an automated tool for auxiliary diagnosis and treatment, forming the foundation of wise information technology of medicine. chinese word segmentation (cws) and medical concept recognition are two important and related tasks for chinese mlp, which received much attention in previous studies @xcite . the first task (i.e., cws) aims to segment chinese text (i.e., character sequence) into words, which is a necessary step for mlp because the meaning of many medical terms cannot be simply inferred by its component characters. for example, it is hard to infer the meaning of "扁桃体" (tonsil) from its components "扁" (flat), "桃" (peach), and "体" (body). the second task (i.e., medical concept recognition) assigns an emr-related tag (e.g., organism and group) to the segmented words. it is worth noting that the medical concept in this paper includes not only the standard medical named entities but also other categories that are useful for medical text analysis. for example, "time" is a medical concept that can be used to represent the disease history; "probability" is a possible medical concept tag for "考虑" (consider), in emr. to perform cws and medical concept recognition in chinese emr, researchers face a challenge that existing training data for the tasks is either publicly unavailable or of poor quality. although one possible solution is to apply models trained in the general domain to the medical text, these models always fail to have good performance because there are many domain-specific medical terms that rarely occur in the general domain. to address these challenges, we collect and annotate a new chinese emr corpus, named acemr, where texts from 500 emrs (7k sentences) are annotated with cws and medical concept recognition labels. in addition, we test several state-of-the-art models for cws and medical concept recognition on the collected acemr corpus. experimental results show the necessity of constructing an informative chinese medical corpus and provide certain guidance for the model selection in medical domain. 2 related work nlp for medical text has draw many attentions in the recent years @xcite @xcite @xcite @xcite , especially for the emr texts. among different tasks to process chinese emr texts, cws and medical concept recognition are two fundamental ones that draw much attentions from previous studies. due to the dramatic performance drop when applying the model trained from open source corpus on the medical field, previous studies @xcite @xcite @xcite always construct chinese medical datasets themselves and test their models on the datasets. however, most constructed datasets used for cws are relatively small, where there are only roughly 100 chinese emrs. besides, the medical concept types in most existing datasets are limited to named entities (e.g., "disease" and "symptoms and signs"), which fails to consider other medical concept types (e.g., "time") in emrs that are potentially helpful for chinese emr texts analysis. ## methods a good text representation is highly important in achieving a promising performance in many nlp tasks @xcite @xcite . therefore, we select several well-known models for cws and medical concept recognition tasks and test them on acemr corpus. ## experiments in the experiments, we use two datasets. the first is the in-domain acmer corpus introduced in sec. 3; the second is ctb6 @xcite , which is a benchmark cws dataset of the general domain text. we split the acmer corpus into training/test sets and report the statistics in ## conclusion in this paper, we collect a new chinese medical corpus, named acemr, which contains 500 emrs from a local hospital, and annotate the corpus with cws and medical concept labels. acemr features in the rich types of medical concept, in which 20 sub-classes of medical concepts are annotated. we test several state-of-the-art models for cws and medical concept recognition on the annotated acemr. the results on cws show that models trained on general domain dataset (i.e., ctb6) cannot perform well on medical domain, which confirms the necessity of constructing the acemr corpus. furthermore, wmseg with wordhood information and twasp with auto-generated syntactic information outperforms strong baselines on word segmentation and medical concept recognition, respectively, which demonstrates the benefit of leveraging extra resources (i.e., wordhood information and syntactic information) for cws and medical concept recognition.