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QuickUMLS: a Fast, Unsupervised Approach for Medical Concept Extraction

TLDR
The proposed method achieves similar precision and recall of state-of-the-art systems on two clinical notes corpora, and outperforms MetaMap and cTAKES on a dataset of consumer drug reviews up to 135 times faster than both systems.

Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing

TLDR
A unified architecture based on Sequence to Sequence models and Pointer Generator Network to handle both simple and complex queries is proposed and achieves state of the art performance on three publicly available datasets.
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SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions

TLDR
This paper investigates the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtains high-quality labeled data without the need for manual labelling.
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The Cascade Transformer: an Application for Efficient Answer Sentence Selection

TLDR
The Cascade Transformer is introduced, a simple yet effective technique to adapt transformer-based models into a cascade of rankers, each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time.
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On clinical decision support

TLDR
This work investigates the utility of applying pseudo-relevance feedback (PRF), a query expansion method that performs well in keyword-based medical literature search to CDS search, and obtains statistically significant retrieval efficiency improvement in terms of nDCG, over the baseline.
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Enhancing web search in the medical domain via query clarification

TLDR
The utility of bridging the gap between layperson and expert vocabularies is investigated and the approach adds the most appropriate expert expression to queries submitted by users, a task the authors call query clarification.
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Retrieving Medical Literature for Clinical Decision Support

TLDR
This work applies query reformulation techniques to address literature search based on case reports and achieves a statistically significant improvement over the baseline and the state-of-the-art.
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Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-Shot Learning

TLDR
The proposed approach can significantly outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and Spanish and it is shown that augmenting the English training collection with some examples from the target language can sometimes improve performance.
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Query Reformulation for Clinical Decision Support Search

TLDR
This work presents a query reformulation approach that addresses the unique formulation of case reports, making them suitable to be used on a general purpose search engine.
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Learning to Rank for Consumer Health Search: A Semantic Approach

TLDR
A Learning to Rank system that uses a novel set of syntactic and semantic features to improve consumer health search and was evaluated on the 2016 CLEF eHealth dataset, outperforming the best method.
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