Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.

Importance: To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify on a large scale. Objective: To develop and compare 2 natural language processing methods, a rules-based approach and a machine learning (ML) approach, for identifying bleeding events in clinical notes. Design, Setting, and Participants: This diagnostic study used deidentified notes from the Medical Information Mart for Intensive Care, which spans 2001 to 2012. A training set of 990 notes and a test set of 660 notes were randomly selected. Physicians classified each note as present or absent for a clinically relevant bleeding event during the hospitalization. A bleeding dictionary was developed for the rules-based approach; bleeding mentions were then aggregated to arrive at a classification for each note. Three ML models (support vector machine, extra trees, and convolutional neural network) were developed and trained using the 990-note training set. Another instance of each ML model was also trained on a sample of 450 notes, with equal numbers of bleeding-present and bleeding-absent notes. The notes were represented using term frequency-inverse document frequency vectors and global vectors for word representation. Main Outcomes and Measures: The main outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for each model. Following training, the models were tested on the test set and sensitivities were compared using a McNemar test. Results: The 990-note training set represented 769 patients (296 [38.5%] female; mean [SD] age, 67.42 [14.7] years). The 660-note test set represented 527 patients (211 [40.0%] female; mean [SD] age, 67.86 [14.7] years). Bleeding was present in 146 notes (22.1%). The extra trees down-sampled model and rules-based approaches were similarly sensitive (93.8% vs 91.1%; difference, 2.7%; 95% CI, -3.8% to 7.9%; P = .44). The positive predictive value for the extra trees model, however, was 48.6%. The rules-based model had the best performance overall, with 84.6% specificity, 62.7% positive predictive value, and 97.1% negative predictive value. Conclusions and Relevance: Bleeding is a common complication in health care, and these results demonstrate an automated and scalable detection method. The rules-based natural language processing approach, compared with ML, had the best performance in identifying bleeding, with high sensitivity and negative predictive value.