Detection of infectious symptoms from VA emergency department and primary care clinical documentation

The majority of clinical symptoms are stored as free text in the clinical record, and this information can inform clinical decision support and automated surveillance efforts if it can be accurately processed into computer interpretable data.

We developed rule-based algorithms and evaluated a natural language processing (NLP) system for infectious symptom detection using clinical narratives. Training (60) and testing (444) documents were randomly selected from VA emergency department, urgent care, and primary care records. Each document was processed with NLP and independently manually reviewed by two clinicians with adjudication by referee. Infectious symptom detection rules were developed in the training set using keywords and SNOMED-CT concepts, and subsequently evaluated using the testing set.

Overall symptom detection performance was measured with a precision of 0.91, a recall of 0.84, and an F measure of 0.87. Overall symptom detection with assertion performance was measured with a precision of 0.67, a recall of 0.62, and an F measure of 0.64. Among those instances in which the automated system matched the reference set determination for symptom, the system correctly detected 84.7% of positive assertions, 75.1% of negative assertions, and 0.7% of uncertain assertions.

This work demonstrates how processed text could enable detection of non-specific symptom clusters for use in automated surveillance activities.