## 4 Discussion  
In the present study, we developed a ConBERT model using word-and character-level BERT to automate the standardization of operative reports written in free text in electronic medical records with an acceptable prediction capability. Most existing studies have predicted the ICD-9 code with the MIMIC-III dataset using detailed descriptions of diseases such as discharge summaries [25]. In the proposed model, an F1 score of 0.7415 was achieved by predicting the ICD-9 code using only the operative report and diagnosis. Accordingly, by matching various operative reports with ICD-9 codes, surgeons can provide  
Figure 4: Multi-prediction page of the web-based application. Figure 3: Single prediction page of the web-based application. accurate and standardized data automatically, without modifying the existing methods of recording operative reports in free text. Moreover, manual standardization depends on the quality of the mapping tables or skills, which can differ depending on the individual or the institution. Our model can replace inefficient labor and provide more consistent and accurate standardization of operative reports. The proposed model utilizes the advantages of both word- and character-level BERT models to solve problems, including non-standardized operative reports, typographical errors, and abbreviations, caused by the data characteristics of surgical records. Thus, compared with the previous studies, the performance was 15-16% higher based on the micro F1 score and 27-29% higher based on the macro F1 score (Table 7). The common data model (CDM), based on common standard terms, was previously proposed as a healthcare data model according to a standardized machine learning framework [26, 27]. Observational Health Data Science and Informatics is an international network of researchers and data partners that maintains an analytical solution, the Observational Medical Outcome Partnership (OMOP) CDM, which transforms diverse and heterogeneous databases worldwide into a common format [28, 29]. The OMOP CDM enables researchers to investigate standardized content with high consistency and extensibility. The operative report, which contains treatment-related patient information, can be utilized in medical research as an indicator to evaluate the appropriateness of treatments and establish an insurance policy. Therefore, the CDM should include accurate operative reports; therefore, standardization of the operative reports written in free text according to the ICD codes is imperative for building the CDM. Our BERT-based model can serve as an excellent tool to standardize operative reports. Although various tools have been developed to help researchers automatically transform data into standard terminologies [30, 31], to the best of our knowledge, a model for standardizing operative reports does not exist. Our model can facilitate a more efficient mapping of operation data to the CDM. However, our study had several limitations. First, approximately 3% of the data were removed. Although the use of removed data is unlikely in an actual hospital setting, this can hinder accurate automated collection. Therefore, in subsequent studies, it is necessary to collect more data and secure data with a single label to solve this problem. Second, our dataset is highly imbalanced. We conducted learning by giving weights for each class through data-level approaches, but it was confirmed that there is a trade-off in which macro performance increases and micro performance decreases. Further research on data imbalance is needed using weight balancing techniques, or more advanced training loss such as focal loss [32] or self-adjusting dice loss [33]. Third, the study data were limited to patients from the department of surgery. For better performance and predictability, it is necessary to integrate data from multiple institutions and other departments, such as orthopedics and neurosurgery, to expand the scope of our model.