Allows automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. DiseaseExtract includes two broad stages: (i) extracting disease mentions from free text–a task referred to as disease named entity recognition (DNER) and (ii) normalizing the recognized mentions to standard controlled vocabularies such as MeSH–a task referred to as disease name normalization (DNORM).
School of Public Health and Community Medicine, UNSW, Kensington, NSW, Australia; Prince of Wales Clinical School, UNSW, Kensington, NSW, Australia; Institution of Information Science, Academia Sinica, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taitung University, Taipei, Taiwan
DiseaseExtract funding source(s)
This project was supported as part of the electronic Practice Based Research Network (ePBRN) and Translational Cancer research network (TCRN) research programs. It was also funded in part by the
School of Public Health & Community Medicine, Ingham Institute for Applied Medical Research, UNSW Medicine and South West Sydney Local Health District; the Cancer Institute of New South Wales and Prince of Wales Clinical School, UNSW Medicine.