Assigning categorical information to Japanese medical terms using MeSH and MEDLINE.

This paper reports on the assigning of MeSH (Medical Subject Headings) categories to Japanese terms in an English-Japanese dictionary using the titles and abstracts of articles indexed in MEDLINE. In a previous study, 30,000 of 80,000 terms in the dictionary were mapped to MeSH terms by normalized comparison. It was reasoned that if the remaining dictionary terms appeared in MEDLINE-indexed articles that are indexed using MeSH terms, then relevancies between the dictionary terms and MeSH terms could be calculated, and thus MeSH categories assigned.

Finding malignant findings from radiological reports using medical attributes and syntactic information.

Radiology reports are written primarily in natural language. Automated extraction of malignant findings from narrative reports is an important technique for clinical support or alert generation for physicians. This paper proposes a method for automatically extracting malignant findings from narrative radiological reports written in Japanese.

Mapping Japanese medical terms to UMLS Metathesaurus.

This paper introduces and reports the results for a project to map Japanese medical terms to the UMLS Metathesaurus. The "Thesaurus for Medical and Health related Terms version 5" published in 2003 by the Japan Medical Abstracts Society and UMLS version 2002AC provided by NLM were used in this study. The goal was to judge the validity of the correlation between the Japanese and English terms that belong to the same MeSH concept.

Extracting diagnosis from Japanese radiological report.

This study is aimed at extracting diagnosis with positive or negative assertion from radiological report written in Japanese Natural Language. We get frequency of verb patterns that indicate pos/neg assertion, and extract a rule in order of the occurrence. We made customized dictionary of 36,152 terms relating to disease names or radiological findings, and tried to extract pairs of (pos/neg, disease and verb pattern ) by using rules according to the most frequent pattern from 1,524/5,000 CT reports (each report consists of 15.