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Overview of the TREC 2014 Clinical Decision Support Track
The focus of the 2014 track was the retrieval of biomedical articles relevant for answering generic clinical questions about medical records, using short case reports, such as those published in biomedical articles, as idealized representations of actual medical records. Expand
EmpaTweet: Annotating and Detecting Emotions on Twitter
A corpus collected from Twitter with annotated micro-blog posts annotated at the tweet-level with seven emotions: ANGER, DISGUST, FEAR, JOY, LOVE, SADNESS, and SURPRISE is introduced. Expand
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
A deep learning model is presented to efficiently detect a disease from an image and annotate its contexts (e.g., location, severity and the affected organs), and a novel approach to use the weights of the already trained pair of CNN/RNN on the domain-specific image/text dataset, to infer the joint image/ text contexts for composite image labeling. Expand
TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of theExpand
Automatic extraction of relations between medical concepts in clinical texts
Lexical and contextual features proved to be very important in relation extraction from medical texts and contributed to a decrease in the F1 score when they are not available. Expand
Recognizing Textual Entailment with LCC ’ s G ROUNDHOG System
We introduce a new system for recognizing textual entailment (known as GROUNDHOG) which utilizes a classification-based approach to combine lexico-semantic information derived from text processingExpand
State-of-the-art in biomedical literature retrieval for clinical cases: a survey of the TREC 2014 CDS track
An overview of the task, a survey of the information retrieval methods employed by the participants, an analysis of the results, and a discussion on the future directions for this challenging yet important task are provided. Expand
Overview of the TREC 2020 Precision Medicine Track
The TREC Precision Medicine track was launched to specialize the CDS track to the needs of precision medicine so IR systems can focus on this important issue, and for three years the TREC Clinical Decision Support track sought to evaluate IR systems that provide medical evidence at the point ofcare. Expand
Enhancing Clinical Concept Extraction with Contextual Embedding
The potential of contextual embeddings is demonstrated through the state-of-the-art performance these methods achieve on clinical concept extraction and the impact of the pretraining time of a large language model like ELMo or BERT is analyzed. Expand
TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19
TREC-COVID differs from traditional IR shared task evaluations with special considerations for the expected users, IR modality considerations, topic development, participant requirements, assessment process, relevance criteria, evaluation metrics, iteration process, projected timeline, and the implications of data use as a post-task test collection. Expand