Unified extraction of health condition descriptions

Authors:
Ivelina Nikolova
Affiliations:
Bulgarian Academy of Sciences, Sofia
Venue:
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Year:
2012

Citing 2
Cited 1

ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports

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The WEKA data mining software: an update

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Automatic information extraction from patient records in Bulgarian language

Proceedings of the 14th International Conference on Computer Systems and Technologies

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Abstract

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 "canonical forms" exist in any dictionary. The focus 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 performed on 2031 sentences of diabetes case history. The F-measure varies between 60 and 96% in the separate processing phases.