Approaches for Information Extraction from Electronic Patient Records

Clinical medical records contain a wealth of information, largely in free-textual form. Thus, means to extract structured information from free-text records becomes an important research endeavor. In this paper, we implement an information extraction (IE) system that extracts a variety of information of patients with breast complaints from their semi-structured patient records. Three approaches are proposed to solve different IE tasks and very good performance (precision and recall) is achieved. A novel graph-based approach, which uses the parsing result of link-grammar parser, was invented for concept association, and extremely 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 is used to perform text classification. This preliminary approach to categorical fields has, so far, proven to be quite effective