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Updated: Jun 27, 2026
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Development of a medical-text parsing algorithm based on character adjacent probability distribution for Japanese
N Nishimoto1, S Terae, M Uesugi
1Department of Medical Informatics, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan. nishimot@med.hokudai.ac.jp
Objectives:
The objectives of this study were to investigate the transitional probability distribution of medical term boundaries between characters and to develop a parsing algorithm specifically for medical texts.
Methods:
Medical terms in Japanese computed tomography (CT) reports were identified using the ChaSen morphological analysis system. MeSH-based medical terms (51,385 entries), obtained from the metathesaurus in the Unified Medical Language System (UMLS, 2005AA), were added as a medical dictionary for ChaSen. A radiographer corrected the set of results containing 300 parsed CT reports. In addition, two radiologists checked the medical term parsing of 200 CT sentences.
Results:
We obtained modified inter-annotator agreement scores for the text corrected by the radiologists. We retrieved the transitional probability as the conditional probability of a uni-gram, bi-gram, and tri-gram. The highest transitional probability P(Ci | Ci- 2(*)Ci- 1) was 1.00. For an example of anatomical location, the term "pulmonary hilum" was parsed as a tri-gram.
Conclusions:
Retrieval of transitional probability will improve the accuracy of parsing compound medical terms.
