title: Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.
abstract: The second track of the 2014 i2b2/UTHealth natural language processing shared task focused on identifying medical risk factors related to Coronary Artery Disease (CAD) in the narratives of longitudinal medical records of diabetic patients. The risk factors included hypertension, hyperlipidemia, obesity, smoking status, and family history, as well as diabetes and CAD, and indicators that suggest the presence of those diseases. In addition to identifying the risk factors, this track of the 2014 i2b2/UTHealth shared task studied the presence and progression of the risk factors in longitudinal medical records. Twenty teams participated in this track, and submitted 49 system runs for evaluation. Six of the top 10 teams achieved F1 scores over 0.90, and all 10 scored over 0.87. The most successful system used a combination of additional annotations, external lexicons, hand-written rules and Support Vector Machines. The results of this track indicate that identification of risk factors and their progression over time is well within the reach of automated systems.
authors.affiliation: School of Library and Information Science, Simmons College, Boston, MA, USA. Electronic address: stubbs@simmons.edu.
authors.forename: Amber
authors.initials: A
authors.lastname: Stubbs
authors.affiliation: Department of Information Studies, State University of New York at Albany, Albany, NY, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Kotfila
authors.affiliation: Center for Computational Biomedicine, University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
authors.affiliation: Department of Information Studies, State University of New York at Albany, Albany, NY, USA.
authors.forename: Özlem
authors.initials: Ö
authors.lastname: Uzuner
mesh_terms: D000368:Aged; D001900:Boston / Q000453:epidemiology; D015331:Cohort Studies; D015897:Comorbidity; D016494:Computer Security; D003219:Confidentiality; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000453:epidemiology*; 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; D018875:Vocabulary, Controlled
keywords: CAD; Clinical narratives; Diabetes; Natural language processing