## 1 Introduction  
In Japan, medical facilities and medical device manufacturers are required to submit medical device adverse event reports (MDAERs) to the Ministry of Health, Labor, and Welfare when medical devices are involved in adverse events such as catheter breakage occurring during a medical procedure. In MDAERs, a detailed description of the adverse events and the problems they give rise to for patients due to the medical devices are obtained through free writing (there are no guidelines for the written format); and it is difficult to categorize the cases involved for a statistical analysis of the adverse events.  
Therefore, to standardize the terms in MDEARs, medical device adverse event terminology 1 st edition (terminology of the Japan Federation of Medical Devices Associations (JFMDA terminology)) was published in March 2015 [1]. This terminology consists of 89 medical device terminology items developed by 13 industry groups in the industry that are members of the JFMDA. Each terminology item most commonly has three parts: medical device problems, patient problems, and components involved. In addition, each term of the \"medical device problem\" and \"patient problem\" categories have definition sentences, synonyms, and the term code of the Center for Devices and Radiological Health (CDRH) terminology (FDA code) [2], as shown in Figure 1.  
The 13 industry groups independently created each of the terminology entries using a bottom-up approach by gathering the terms used regularly in medical facilities to facilitate communication between medical staff and medical device manufacturers. We are working on the mapping of these terminologies to ensure that they are consistent. In a previous study, we integrated the 89 terminology items using the resource description framework (RDF), based on the spelling of the terms [3]. One problem associated with mapping terminologies is that the cases where terms are considered to represent the same concept are included with different spellings (synonyms). There are about 3500 terms related to medical device problems, and manually conducting the verification/qualification requires great effort.  
We focused on the definition sentences to detect synonym pairs with different spelling. There are two main approaches to detect similar sentences: edit distance and distributed representation. The edit distance is an algorithm for quantifying how two dissimilar strings are related by counting the minimum number of operations required to transform one string into the other. This algorithm was used to map terms between the International Classification of Diseases (ICD) code and clinical text [4] and to detect misspellings in the text [5]. The advantages of this algorithm are misspelling detection and normalization of clinical terms. Hence, there is a possibility that morphological and typographical variations in the definition sentences in JFMDA terminology can be identified effectively.  
In distributed representation, named Word2 vec [6] and Doc2 vec [7], compute vectors of words and documents using simple neural networks with context information. The similarities between words and documents are calculated via the cosine similarity. The merit of distributed representation is embedding the concept of words as vectors, and this algorithm can detect synonyms with different spellings. There are some previous medical studies for synonym identification [8; 9; 10]. Some studies used Doc2 vec, which is a method for conversion from documents to vectors in order for them to be applied in the detection of similar text [11; 12; 13] and allows mapping among the standard codes [14]. We believe that distributed representations can be applied to identify synonyms efficiently, and it is necessary to evaluate the applicability to our task. The purpose of our study is to detect synonyms automatically for JFMDA terminology items and compare the accuracy of the detection among distributed representations and edit distances.