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QuickUMLS: a Fast, Unsupervised Approach for Medical Concept Extraction
Entity extraction is a fundamental step in many health informatics systems. In recent years, tools such as MetaMap and cTAKES have been widely used for medical concept extraction on medicalExpand
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. Expand
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. Expand
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. Expand
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. Expand
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. Expand
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. Expand
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. Expand
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. Expand
Characterizing Question Facets for Complex Answer Retrieval
TLDR
This work presents two novel approaches for CAR based on the observation that question facets can vary in utility: from structural to topical, and a general approach to reform the structure of ranking models to aid in learning of facet utility in the query-document term matching phase. Expand
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