AuDis: an automatic CRF-enhanced disease normalization in biomedical text.
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: http://ikmlab.csie.ncku.edu.tw/CDR2015/AuDis.html.
Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan, R.O.C.
Hsin-Chun
HC
Lee
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.
Yi-Yu
YY
Hsu
Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan, R.O.C Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C hykao@mail.ncku.edu.tw.
Hung-Yu
HY
Kao
D035843:Biomedical Research / Q000592:standards*; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods* / Q000592:standards*; D016208:Databases, Factual; D004194:Disease; D006801:Humans; D020407:Internet; D009323:Natural Language Processing; D018875:Vocabulary, Controlled