## 1 Introduction  
Remarkable and steady developments in medical research and tools have led to continuous improvements in surgical treatment. Currently, various surgeries are performed in different fields. In general surgery, for example, an identical procedure can be performed using either an open approach or minimally invasive tools such as laparoscopy and robotic systems [1]. Additionally, different types of operative reports for rectal cancer, including low anterior resection or ultra-low anterior resection, can be performed for different individuals, depending on the distance from the aus to the location of rectal cancer [2]. Therefore, exact documentation of operation records is essential for providing appropriate information.  
The operative report, which denotes a representative procedure such as \"appendectomy\" or \"cholecystectomy\" is an important component of the surgical record because it contains key surgical information. The operative report is dictated immediately after an operation and is included in the patient's electronic health record. It contains important information, such as the type of surgery and the method of approach. Surgical records are primarily created by surgeons; however, their documentation varies substantially. Surgeons generally record operative reports in free text and use non-standard abbreviations (for example, appendectomy, laparoscopic appendectomy, lap. appendectomy, lapaappe, appendectomy, and laparoscopic). Nonstandard terminology in operative reports can significantly affect data quality and confuse physicians. Therefore, the standardization of operative reports is an important topic to be analyzed as it plays a critical role in clinical research and quality assurance in healthcare. The International Classification of Diseases (ICD), which is a database comprising clinical terminologies managed by the World Health Organization, is widely accepted as a standardized medical terminology guide [3]. Standardization of operative reports implies matching unstructured data from operative reports written in free text with structured data such as ICD codes. However, the standardization of operative reports requires manual chart review, which is a time- and effort-consuming process.  
Recently, active research [4; 5; 6; 7; 8] is being conducted to automate ICD-9 codes. For this purpose, most studies use clinical notes and discharge summaries, which are information-intensive data sources. To the best of our knowledge, no study has attempted to standardize ICD-9 codes using data sources containing lesser information, such as operative reports and diagnoses. Hence, we considered a bidirectional encoder representation from transformers (BERT)-based model to predict the ICD-9 code using operative reports and diagnoses. BERT [9] demonstrated remarkable performance in various natural language processing fields. However, BERT trained on general domain corpora showed relatively low performance on medical data. Therefore, BERTs trained in biomedical corpora are provided separately. In addition, given the nature of clinical data, there are frequent typographical errors, new words, and abbreviations, and the current word-level BERT is unsuitable. Therefore, character BERT, also known as character-level BERT, has been proposed [10]. Although it efficiently managed a small amount of data, there were errors/limitations when handling a large amount of data. Therefore, we propose a concatenation of BERT (ConBERT) model that utilizes both word- and character-level BERT for ICD-9 code automation. The proposed model showed AP score of 0.7672, F1 score of 0.7415, and AUC of 0.9842 for ICD-9 code automation. We have publicly provided this service as a web-based application accessible at [[http://opti.ziovision.ai/](http://opti.ziovision.ai/)]([http://opti.ziovision.ai/](http://opti.ziovision.ai/)) (accessed on 8 September 2022).