A contextual multi-task neural approach to medication and adverse events identification from clinical text.

Effective wide-scale pharmacovigilance calls for accurate named entity recognition (NER) of medication entities such as drugs, dosages, reasons, and adverse drug events (ADE) from clinical text. The scarcity of adverse event annotations and underlying semantic ambiguities make accurate scope identification challenging. The current research explores integrating contextualized language models and multi-task learning from diverse clinical NER datasets to mitigate this challenge. We propose a novel multi-task adaptation method to refine the embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT) language model to improve inter-task knowledge sharing. We integrated the adapted BERT model into a unique hierarchical multi-task neural network comprised of the medication and auxiliary clinical NER tasks. We validated the model using two different versions of BERT on diverse well-studied clinical tasks: Medication and ADE (n2c2 2018/n2c2 2009), Clinical Concepts (n2c2 2010/n2c2 2012), Disorders (ShAReCLEF 2013). Overall medication extraction performance enhanced by up to +1.19 F1 (n2c2 2018) while generalization enhanced by +5.38 F1 (n2c2 2009) as compared to standalone BERT baselines. ADE recognition enhanced significantly (McNemar's test), out-performing prior baselines. Similar benefits were observed on the auxiliary clinical and disorder tasks. We demonstrate that combining multi-dataset BERT adaptation and multi-task learning out-performs prior medication extraction methods without requiring additional features, newer training data, or ensembling. Taken together, the study contributes an initial case study towards integrating diverse clinical datasets in an end-to-end NER model for clinical decision support.

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

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.