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

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.