title: Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.
abstract: This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system.
authors.affiliation: Linguamatics Ltd., 324 Cambridge Science Park, Milton Road, Cambridge CB4 0WG, UK. Electronic address: james.cormack@linguamatics.com.
authors.forename: James
authors.initials: J
authors.lastname: Cormack
authors.affiliation: Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 N. Lake Shore Drive, 11th Floor, Chicago, IL 60611, USA.
authors.forename: Chinmoy
authors.initials: C
authors.lastname: Nath
authors.affiliation: Linguamatics Ltd., 324 Cambridge Science Park, Milton Road, Cambridge CB4 0WG, UK.
authors.forename: David
authors.initials: D
authors.lastname: Milward
authors.affiliation: Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 N. Lake Shore Drive, 11th Floor, Chicago, IL 60611, USA.
authors.forename: Kalpana
authors.initials: K
authors.lastname: Raja
authors.affiliation: Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 N. Lake Shore Drive, 11th Floor, Chicago, IL 60611, USA.
authors.forename: Siddhartha R
authors.initials: SR
authors.lastname: Jonnalagadda
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology*; D015331:Cohort Studies; D015897:Comorbidity; D016494:Computer Security; D003219:Confidentiality; D057225:Data Mining / Q000379:methods*; D048909:Diabetes Complications / Q000175:diagnosis / Q000453:epidemiology*; D057286:Electronic Health Records / Q000458:organization & administration*; D005260:Female; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D033262:Narration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D018570:Risk Assessment / Q000379:methods; D006113:United Kingdom / Q000453:epidemiology; D018875:Vocabulary, Controlled
keywords: Clinical natural language processing; Information extraction; Text mining