Semi-automatic Construction of a Word Complexity Lexicon for J apanese Medical Terminology (2024)

Abstract: We construct a word complexity lexicon for medical terms in Japanese.To facilitate communication between medical practitioners and patients, medical text simplification is being studied.Medical text simplification is a natural language processing task that paraphrases complex technical terms into expressions that patients can understand.However, in contrast to English, where this task is being actively studied, there are insufficient language resources in Japanese.As a first step in advancing research on medical text simplification in Japanese, we annotate the 370,000 words from a large-scale medical terminology lexicon with a five-point scale of complexity for patients.

## introduction communication between medical practitioners and patients is important to facilitate understanding of the diagnosis and agreement on a treatment plan @xcite . one of the factors that make communication difficult in the medical field is the difference in expertise between medical practitioners and patients. in particular, since many medical terms are difficult for patients to understand, medical practitioners are expected to paraphrase them into simple expressions to make them easier to understand. to solve this problem, medical text simplification @xcite @xcite has been studied, mainly in english. however, there is a lack of available lexicons and corpora for medical text simplification in japanese. in this study, as a first step to tackle japanese medical text simplification, we construct a complexity lexicon for medical terms. we first recruited 40 annotators, who were not medical practitioners via crowdsourcing to survey word complexity for 10,000 medical terms. as a result, we found that the number of unknown medical terms decreased with age and that men tended to be unaware of medical terms related to pregnancy and childbirth, among other characteristics observed for each of the attributes of the annotators. furthermore, we trained a complexity estimation model for medical terms using machine learning with features such as character types, word frequencies, and word embeddings, and achieved higher performance than existing methods. finally, as shown in ## related work large-scale word complexity lexicons in english have been constructed using two approaches. one is to estimate word complexity using the log ratio of the probability of word occurrence in the normal and simple corpora @xcite . the other is to manually annotate word complexity for a subset of the vocabulary and train a word complexity estimation model using these annotations @xcite . in japanese, the former approach cannot be applied because of the unavailability of a large-scale corpus written in simple language. therefore, this study takes the latter approach to construct a word complexity lexicon. in japanese, a domain-independent word complexity estimation model has been proposed that employs character types, word frequencies, and word embeddings as features @xcite . for word complexity estimation specific to the medical domain, a method that takes into account the number of characters and morphemes has been proposed @xcite . similar to these previous studies, we train a machine learning-based word complexity estimator. ## word complexity estimation we train a machine learning-based word complexity estimation model in addition to the three ba-sic features used in the previous study @xcite , with three proposed features. as in previous studies @xcite , we use the support vector machine (svm) model foot_3 for machine learning. foot_4 ## experiments and results we train and evaluate word complexity estimation models using complexity annotations for 10,000 medical terms. ## conclusion in this study, we trained a word complexity estimation model based on word complexity annotations of 10,000 japanese medical terms by 40 non-medical practitioners. our word complexity annotations revealed that even though the number of unknown medical terms decreases with increasing age, more than 70% of medical terms are difficult to understand, even for those in their 50s. experiments on word complexity estimation revealed that features of word frequencies and word embeddings obtained from a large-scale web corpus are useful. finally, we developed a word complexity estimator for japanese medical terms that can classify five levels of complexity with about 80% accuracy, and released a word complexity lexicon 2 covering about 370,000 japanese medical terms. although this study focused on disease and symptom names in japanese, our future work includes the application of complexity estimation to more diverse medical terminology, such as drug names and names of human body parts. note that the "word complexity" in this study was judged by the patients themselves. even if the patients themselves consider it to be simple, it is possible that medical misunderstandings may have occurred.