Unified concept and assertion detection using contextual multi-task learning in a clinical decision support system

[Display omitted] •Integration of hierarchy-based fine-tuning and multi-task learning.•Enhanced concept and assertion detection on several real-world clinical notes datasets.•Negation out-performance with as few as 300 training examples compared to standalone models.•Speculation out-performance in generalization tests.•Clinical terms expressing implicit negation remain a challenge. Assertions, such as negation and speculation, alter the meaning of clinical findings (‘concepts’) in Electronic Health Records. Accurate assertion detection is vital to the identification of target findings in clinical decision support systems. Diverse clinical concepts and assertion modifiers embedded within longer sentences add to the challenge of error-free detection. Recent approaches leveraging biomedical contextual embeddings lead to standalone concept and assertion models that do not effectively utilize inter-task knowledge transfer. We propose a novel neural model integrating task-specific fine-tuning and multi-task learning in a coherent framework based on the hierarchical relationship between the tasks. We show that such a unified framework enhances both the tasks using several real-world clinical notes’ datasets (n2c2 2010, n2c2 2012, NegEx). Concept task performance enhanced by +1.69 F1 on n2c2 2010 and +2.96 F1 on n2c2 2012 compared to standalone baselines. Assertion recognition improved by +2.89 F1 and +3.77 F1, respectively. Negation detection under low-resource settings increased significantly (+2.4 F1, p-value = 3.11E−05, McNemar’s test), demonstrating the impact of inter-task knowledge transfer. The integrated architecture enhanced the generalization performance of speculation detection (+2.09 F1). To the best of our knowledge, this model is the first demonstration of a contextual multi-task system for unified detection of concepts and assertions in clinical decision support applications.