Paper: Unified Extraction of Health Condition Descriptions

ACL ID N12-2005
Title Unified Extraction of Health Condition Descriptions
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Student Session
Year 2012
Authors

This paper discusses a method for identifying diabetes symptoms and conditions in free text electronic health records in Bulgarian. The main challenge is to automatically recognise phrases and paraphrases for which no ?canon- ical forms? exist in any dictionary. The fo- cus is on extracting blood sugar level and body weight change which are some of the dominant factors when diagnosing diabetes. A combined machine-learning and rule-based approach is applied. The experiment is per- formed on 2031 sentences of diabetes case his- tory. The F-measure varies between 60 and 96% in the separate processing phases.