Encoder-Decoderモデルを用いた医学用語・フレーズの自動正規化

抄録

With the progress of digitization in medical care, various medical texts, such as electronic medical record texts, case report texts, discharge summaries, and radiology reading reports, are being accumulated electronically. It is expected that medical knowledge can be extracted from these electronic medical texts and utilized for secondary purposes such as diagnostic support. In fact, in Japan, there is a case (J-CaseMap) where a case report text from a regional meeting of the Japanese Society of Internal Medicine was used as a diagnostic support system to search for diseases and pathological conditions that can be used as a reference for differential diagnosis. On the other hand, it is said that half of the medical knowledge becomes outdated in about five years, and new knowledge emerges and changes rapidly every day. Therefore, it is necessary to update the knowledge database constructed from electronic medical texts as needed. However, the construction and updating of a structured database requires a lot of expert manpower, making efficiency and automation a major issue. In fact, in J-CaseMap, the construction of diseases, pathological conditions, symptom findings, and causal chains among them are all done manually. The process of automatically constructing causal chain knowledge from case report texts involves three major steps: (1) extracting important terms and phrases (diseases, pathological conditions, and symptom findings that are important for differential diagnosis) from case report texts, (2) normalizing the extracted expressions to standard terminological expressions, and (3) estimating causal relationships among the normalized terminological expressions. In this study, we focused on the 2nd Step, i.e., normalization of important expressions extracted from case report sentences into standardized terminological expressions, utilizing an Encoder-Decoder model. The study also compared each model and analyzed the quality of the output.