Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.
Journal:
Journal of biomedical informatics
Published Date:
Dec 1, 2015
Abstract
We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results.
Authors
Keywords
Aged
Cardiovascular Diseases
Cohort Studies
Comorbidity
Computer Security
Confidentiality
Data Mining
Diabetes Complications
Electronic Health Records
Female
Humans
Incidence
Longitudinal Studies
Male
Middle Aged
Models, Statistical
Narration
Natural Language Processing
Pattern Recognition, Automated
Risk Assessment
Semantics
Vocabulary, Controlled
Wisconsin