The 21st Annual ACM Symposium on Applied Computing 2006, Technical tracks on Computer Applications in Health Care (CAHC 2006), Dijon, France, April 23 -27, 2006. Retrieved 6/21/2006 from http://www.ischool.drexel.edu/faculty/hhan/SAC2006_CAHC.pdf.Clinical medical records contain a wealth of information, largely in free-text form. Means to extract structured information from free-text records is an important research endeavor. In this paper, we describe a MEDical Information Extraction (MedIE) system that extracts and mines a variety of patient information with breast complaints from free-text clinical records. MedIE is a part of medical text mining project being conducted in Drexel University. Three approaches are proposed to solve different IE tasks and very good performance (precision and recall) was achieved. A graph-based approach which uses the parsing result of link-grammar parser was invented for relation extraction; high accuracy was achieved. A simple but efficient ontology-based approach was adopted to extract medical terms of interest. Finally, an NLP-based feature extraction method coupled with an ID3-based decision tree was used to perform text classification
