@yucca:10 JSONTXT

{"target":"http://pubannotation.org/docs/sourcedb/@yucca/sourceid/10","sourcedb":"@yucca","sourceid":"10","text":"Rule-based Entity Recognition and Coverage of SNOMED CT in Swedish Clinical Text.\nNamed entity recognition of the clinical entities disorders, findings and body structures is needed for information extraction from unstructured text in health records.\nClinical notes from a Swedish emergency unit were annotated and used for evaluating a rule- and terminology-based entity recognition system.\nThis system used different preprocessing techniques for matching terms to SNOMED CT, and, one by one, four other terminologies were added.\nFor the class body structure, the results improved with preprocessing, whereas only small improvements were shown for the classes disorder and finding.\nThe best average results were achieved when all terminologies were used together.\nThe entity body structure was recognised with a precision of 0.74 and a recall of 0.80, whereas lower results were achieved for disorder (precision: 0.75, recall: 0.55) and for finding (precision: 0.57, recall: 0.30).\nThe proportion of entities containing abbreviations were higher for false negatives than for correctly recognised entities, and no entities containing more than two tokens were recognised by the system.\nLow recall for disorders and findings shows both that additional methods are needed for entity recognition and that there are many expressions in clinical text that are not included in SNOMED CT.","tracks":[]}