Paper Info
Title
Annotating risk factors for heart disease in clinical narratives for diabetic patients
Abstract
Display Omitted NLP task focused on identifying risk factors over time in clinical narratives.Corpus of 1304 longitudinal medical records for 296 patients.\"Light\" annotation task for domain expert annotators.Gold standard created through voting.Corpus used for track 2 of 2014 i2b2/UTHealth NLP Shared Task. The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a \"light\" annotation paradigm to annotate a set of 1304 longitudinal medical records describing 296 patients for risk factors and the times they were present. We designed the annotation task for this track with the goal of balancing annotation load and time with quality, so as to generate a gold standard corpus that can benefit a clinically-relevant task. We applied light annotation procedures and determined the gold standard using majority voting. On average, the agreement of annotators with the gold standard was above 0.95, indicating high reliability. The resulting document-level annotations generated for each record in each longitudinal EMR in this corpus provide information that can support studies of progression of heart disease risk factors in the included patients over time. These annotations were used in the Risk Factor track of the 2014 i2b2/UTHealth shared task. Participating systems achieved a mean micro-averaged F1 measure of 0.815 and a maximum F1 measure of 0.928 for identifying these risk factors in patient records.
Year
DOI
Venue
2015
10.1016/j.jbi.2015.05.009
Journal of Biomedical Informatics
Keywords
Field
DocType
Annotation,Medical records,Natural language processing
Data mining,Annotation,Information retrieval,Computer science,Subject-matter expert,Risk assessment,Medical record,Gold standard,Documentation,Cohort study,Risk factor
Journal
Volume
Issue
ISSN
58
S
1532-0464
Citations 
PageRank 
References 
22
0.89
12
Authors
2
Name
Order
Citations
PageRank
Amber Stubbs11159.57
Özlem Uzuner2104567.09