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

Key Points Question Can a natural language processing approach that uses text from clinical notes identify bleeding events among critically ill patients? Findings In this diagnostic study of a rules-based natural language processing model to identify bleeding events using clinical notes, the model was superior to a machine learning approach, with high sensitivity and negative predictive value. The extra trees machine learning model had high sensitivity but poor positive predictive value. Meaning Bleeding complications can be detected with a high-throughput natural language processing algorithm, an approach that can be used for quality improvement and prevention programs.