Introduction Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable information regarding ADEs and hence are an important source for detecting ADE signals. However, EHR texts tend to be noisy. Yet applying off-the-shelf tools for EHR text preprocessing jeopardizes the subsequent ADE detection performance, which depends on a well tokenized text input. Objective In this paper, we report our experience with the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE1.0), which aims to promote deep innovations on this subject. In particular, we have developed rule-based sentence and word tokenization techniques to deal with the noise in the EHR text. Methods We propose a detection methodology by adapting a three-layered, deep learning architecture of (1) recurrent neural network [bi-directional long short-term memory (Bi-LSTM)] for character-level word representation to encode the morphological features of the medical terminology, (2) Bi-LSTM for capturing the contextual information of each word within a sentence, and (3) conditional random fields for the final label prediction by also considering the surrounding words. We experiment with different word embedding methods commonly used in word-level classification tasks and demonstrate the impact of an integrated usage of both domain-specific and general-purpose pre-trained word embedding for detecting ADEs from EHRs. ResultsOur system was ranked first for the named entity recognition task in the MADE1.0 challenge, with a micro-averaged F1-score of 0.8290 (official score). Conclusion Our results indicate that the integration of two widely used sequence labeling techniques that complement each other along with dual-level embedding (character level and word level) to represent words in the input layer results in a deep learning architecture that achieves excellent information extraction accuracy for EHR notes.

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This paper describes an iterative participatory curriculum design approach to developing a problem-based STEM curriculum for preschool children. The curriculum aims to teach young children problem-solving using an adapted version of the engineering design process (EDP). Despite evidence showing that a rigorous, integrated STEM curriculum promotes cognitive development and curiosity, very little STEM or engineering instruction occurs in classrooms for three-to five-year-old children, and few studies include teachers in the curriculum design process. Research has shown that, when children experience an engineering curriculum, they show an increase in engagement, in the number of engineering behaviors displayed, and in persistence in completing activities. As well, when teachers are involved in designing curriculum, they are more likely to feel empowered and sustain implementation. Qualitative analysis of semistructured interviews with 13 preschool teachers after the development process showed that teachers who participated in the process perceived increased knowledge and self-efficacy in teaching STEM in their classrooms. These reflections support using a participatory curriculum design approach for empowering teachers and enhancing self-efficacy in teaching STEM to young children. High teacher self-efficacy has been associated with positive classroom outcomes and teacher retention in the profession.

An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug. ADE detection from text can be either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging interdependencies among these two granularities. We instead design a multi-grained joint deep network model MGADE to concurrently solve both ADE tasks MGADE takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification. Our model improves stateof-art F1-score for both tasks: (i) entity recognition of ADE words (12.5% increase) and (ii) ADE sentence classification (13.6% increase) on MADE 1.0 benchmark of EHR notes.

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This study uses data from US Food and Drug Administration (FDA) databases to quantify approval of high-risk cardiovascular devices for use in pediatric populations and assess the clinical evidence supporting the approvals.

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