Classification of Contextual Use of Left Ventricular Ejection Fraction Assessments.
Knowledge of the left ventricular ejection fraction is critical for the optimal care of patients with heart failure. When a document contains multiple ejection fraction assessments, accurate classification of their contextual use is necessary to filter out historical findings or recommendations and prioritize the assessments for selection of document level ejection fraction information. We present a natural language processing system that classifies the contextual use of both quantitative and qualitative left ventricular ejection fraction assessments in clinical narrative documents. We created support vector machine classifiers with a variety of features extracted from the target assessment, associated concepts, and document section information. The experimental results showed that our classifiers achieved good performance, reaching 95.6% F1-measure for quantitative assessments and 94.2% F1-measure for qualitative assessments in a five-fold cross-validation evaluation.
School of Computing, University of Utah, Salt Lake City, USA.
Youngjun
Y
Kim
Department of Biomedical Informatics, University of Utah, Salt Lake City, USA.
Jennifer
J
Garvin
VA Palo Alto Health Care System, Palo Alto, CA, and Stanford University, Stanford, CA, USA.
Mary K
MK
Goldstein
Department of Biomedical Informatics, University of Utah, Salt Lake City, USA.
Stéphane M
SM
Meystre
D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification*; D006333:Heart Failure / Q000175:diagnosis*; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013318:Stroke Volume; D018875:Vocabulary, Controlled