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Prescription information and adverse drug reactions (ADR) are two components of detailed medication instructions that can benefit many aspects of clinical research. Automatic extraction of this information from free-text narratives via Information Extraction (IE) can open it up to downstream uses. IE is commonly tackled by supervised Natural Language Processing (NLP) systems which rely on annotated training data. However, training data generation is manual, timeconsuming, and labor-intensive. It is desirable to develop automatic methods for augmenting manually labeled data. We propose pseudo-data generation as one such automatic method. Pseudo-data are synthetic data generated by combining elements of existing labeled data. We propose and evaluate two sets of pseudo-data generation methods: knowledge-driven methods based on gazetteers and datadriven methods based on deep learning. We use the resulting pseudo-data to improve medication and ADR extraction. Datadriven pseudo-data are suitable for concept categories with high semantic regularities and short textual spans. Knowledgedriven pseudo-data are effective for concept categories with longer textual spans, assuming the knowledge base offers good coverage of these concepts. Combining the knowledge- and data-driven pseudo-data achieves significant performance improvement on medication names and ADRs over baselines limited to the use of available labeled data. [ABSTRACT FROM AUTHOR]

De-identification aims to remove 18 categories of protected health information from electronic health records. Ideally, deidentification systems should be reliable and generalizable. Previous research has focused on improving performance but has not examined generalizability. This paper investigates both performance and generalizability. To improve current state-of-the-art performance based on long short-term memory (LSTM) units, we introduce a system that uses gated recurrent units (GRUs) and deep contextualized word representations, both of which have never been applied to de-identification. We measure performance and generalizability of each system using the 2014 i2b2/UTHealth and 2016 CEGS N-GRID deidentification datasets. We show that deep contextualized word representations improve state-of-the-art performance, while the benefit of switching LSTM units with GRUs is not significant. The generalizability of de-identification system significantly improved with deep contextualized word representations; in addition, LSTM units-based system is more generalizable than the GRUs-based system. [ABSTRACT FROM AUTHOR]

De-identification aims to remove 18 categories of protected health information from electronic health records. Ideally, deidentification systems should be reliable and generalizable. Previous research has focused on improving performance but has not examined generalizability. This paper investigates both performance and generalizability. To improve current state-ofthe- art performance based on long short-term memory (LSTM) units, we introduce a system that uses gated recurrent units (GRUs) and deep contextualized word representations, both of which have never been applied to de-identification. We measure performance and generalizability of each system using the 2014 i2b2/UTHealth and 2016 CEGS N-GRID deidentification datasets. We show that deep contextualized word representations improve state-of-the-art performance, while the benefit of switching LSTM units with GRUs is not significant. The generalizability of de-identification system significantly improved with deep contextualized word representations; in addition, LSTM units-based system is more generalizable than the GRUs-based system. [ABSTRACT FROM AUTHOR]