Se-Hee Park  Il-Seok Oh

10.5626/JOK.2025.52.10.825

clinical trial medical coding Subword Tokenizer MedDRA reported term low-level terms

In clinical trials, the terms reported by investigators for subjects’ medical history and adverse events are called Reported Terms (RT). Since the same symptom can be expressed differently by each investigator, a medical coding process that maps RTs to the MedDRA standard Low-Level Terms (LLTs) is essential. While English-based systems exist, domestic studies face challenges due to the mixture of Korean and English. This study proposes a method to convert RTs, including Korean expressions, into LLTs. We constructed a training dataset of 4,398 RT–LLT pairs collected from domestic clinical trials and applied subword tokenization algorithms—SentencePiece and WordPiece—to handle noise such as multilingual inputs, spacing, typos, and punctuation. Using the token vocabulary generated during training, we segmented new RTs and implemented an algorithm that recommends top-k LLTs based on matching scores. Test results showed that the correct LLT was included within the top five candidate list on average.