AuDis: an automatic CRF-enhanced disease normalization in biomedical text

Hsin Chun Lee, Yi Yu Hsu, Hung Yu Kao

研究成果: Article同行評審

17 引文 斯高帕斯(Scopus)


Diseases play central roles in many areas of biomedical research and healthcare. Consequently, aggregating the disease knowledge and treatment research reports becomes an extremely critical issue, especially in rapid-growth knowledge bases (e.g. PubMed). We therefore developed a system, AuDis, for disease mention recognition and normalization in biomedical texts. Our system utilizes an order two conditional random fields model. To optimize the results, we customize several post-processing steps, including abbreviation resolution, consistency improvement and stopwords filtering. As the official evaluation on the CDR task in BioCreative V, AuDis obtained the best performance (86.46% of F-score) among 40 runs (16 unique teams) on disease normalization of the DNER sub task. These results suggest that AuDis is a high-performance recognition system for disease recognition and normalization from biomedical literature.Database URL:

All Science Journal Classification (ASJC) codes

  • 資訊系統
  • 一般生物化學,遺傳學和分子生物學
  • 一般農業與生物科學


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